Op zondag en in de avonduren geleverd. Following the criteria mentioned above, we selected 11,511 videos collected by about 500 vehicles operated on DiDi’s platform in 5 cities in China. Most existing driving datasets have not paid enough attention to these tasks or were not able to fully address them. Hundreds of thousands of vehicles are operated and online everyday all over China with dashcams which can capture front-facing driving videos, and the number keeps increasing steadily. Monocular pedestrian detection: Survey and experiments. The open-source nature of the videos in the dataset makes them ideal to be used by researchers both in academia and industry. These steps hid few objects such as traffic lights and barely impacted instances of the object classes that we annotated. We propose the D2-City dataset, which is a large-scale driving video dataset collected in China. In this way, we have collected various accident videos with high video quality (720p in resolution). • Video length: The average length of the videos in the CADP dataset is 366 frames per video, which is 3.66x longer than the dataset from [2]. In the future, we plan to release the remaining keyframe annotations, further enlarge the collections of data, and expand the coverage of all-weather scenarios, extreme conditions, and rare cases. Crowded streets: The number of moving cars, motorbikes, and pedestrians per frame are typically larger than other datasets. We also provide bounding boxes and tracking annotations of 12 classes of objects in all frames of 1000 videos and detection annotations on keyframes for the remainder of the videos. 4K dashcam videos versus State of The Art object detection deep nets such as YOLO, SSD or Mask RCNN. W. Tian, Z. Wang, H. Shen, W. Deng, B. Chen, and X. Zhang. Bdd100k: A diverse driving video database with scalable annotation vest a diverse dataset of 678 dashcam accident videos on the web (Fig. Hence, a large number of dashcam videos have been shared on video sharing websites such as YouTube. The numbers of annotated data of these datasets are relatively small, which makes it difficult for models to learn and understand complex scenarios in the real world. The better we are at sharing our knowledge with each other, the faster we move forward. Before we collected videos, we sampled frames from each device and selected devices which constantly produced relatively high quality videos. To supplement these videos, I scraped extra youtube dashcam footage into four second clips, focusing in particular on head-on collisions. pattern recognition. D2-City provides more than 10,000 videos recorded in 720p HD or 1080p FHD from front-facing dashcams, with detailed annotations for object detection and tracking. In practice, we took a 3-step action to keep videos with satisfying quality. Our densely annotated detection and tracking information on 1000 videos made the large video collection more helpful to the field of intelligent driving. Class examples and some specific labeling rules are discussed in Appendix A. The Open Video Scene Detection (OVSD) dataset is an open dataset for the evaluation of video scene detection algorithms. Based on our videos and annotations, we provide training, validation, and testing subsets and three tasks including object detection, multi-object tracking, and large-scale detection interpolation. T.-Y. Pattern Recognition Workshops. The bounding boxes for all cycles (bicycles, motorcycles, and tricycles) only cover the vehicles themselves, no matter riders and passengers are on the vehicles or not. SHARE. The CityScapes Dataset [3] aimed at pixel-level and instance-level semantic labeling tasks and provided 25,000 fully or weakly annotated images. For drivers and passengers in closed-door tricycles and other closed vehicles, we do not annotate them explicitly. In particular, we target at accident videos with human annotated address information or GPS locations. Same as other vision datasets [8, 20], we also added the truncation and occlusion flags for each bounding box in three levels (no, partially, and almost). Je ziet ze steeds meer: dashcams ofwel zo’n bewijsmateriaal verzamelend cameraatje voor je auto. A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, scenes. We expect D2-City not only contains large amount of data but also fully demonstrates the diversity in data. To improve on this situation we want to draw images from existing dashcam videos from all over the world.The resulting dataset will be made available for free to any interested researcher worldwide trying to improve road safety. Top 10 best verkochte dashcams bij Coolblue. The video’s poster and dashcam owner, who asked to only be identified as Sam, said the incident occurred Wednesday night around 6:15 p.m. We randomly split the dataset into training and testing, where the number of training clips is about three times the number of testing clips: 1284 training clips and 466 testing clips. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. Full Text. dataset. ACCV 2016 Oral, Contact : Fu-Hsiang Chan (corgi1205@gmail.com). Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements. Our videos are more challenging than videos in the KITTI dataset due to the following reasons. Finding the best dash cam for you depends on how you intend to use it. single car-mounted camera. In particular, we target at accident videos with human annotated address information or GPS locations. Detailed annotations are costly and time-consuming, especially when we need both per-frame bounding boxes, attributes, and inter-frame tracking labels. It comes with precomputed audio-visual features from billions of … Test: 10 dashcams (2020) Rijdende Rechters. The goal of this dataset is to increase the coverage of road scenarios. M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, This function saves and locks the current video if the car camera detects an accident or a hard-braking incident. We mainly collected videos in the daytime as videos are in better quality especially in terms of light, while about one fifth of all videos are in the early morning or late evening. SHARE. We would thank Haifeng Shen, Xuelei Zhang, and Wanxin Tian for their support on privacy protection. EMAIL. BDD100K [20] collected over 100,000 video clips and provide several types of annotations for key frames of each video. The distributions of the average, maximum, and minimum speeds of all video clips are shown in Figure 5. We manually mark the time-location of accidents and use them as … Officials released video … The KITTI Vision Benchmark Suite [9] proposed several benchmark tasks on stereo, optical flow, visual odometry, 3D object detection and 3D tracking. 3). D2-City contains more than 10,000 video clips which deeply reflect the diversity and complexity of real-world traffic scenarios in China. Second, we carried out spot check on sampled frames everyday to ensure the quality of videos collected from each device on that day. Dashcam of dashboard camera kopen? G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla. TWEET. As videos were recorded by low-cost, daily-use dashcams which are not specifically designed for collecting high-quality data but just for routine recording, they varied a lot in quality. We would thank Wensong Zhang, Yichong Lin, Runbo Hu, Yiping Meng, Menglin Gu, and many other people for useful discussions and general support. Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models. A few dashcams had relatively low angle, tilted angle or partially occluded outside view at times. There are on average 33.48 cars and 8.46 persons in each video. The nuScenes Dataset [15] aimed at providing data from the entire sensor suite, similar to KITTI, rather than only camera-based vision. Tracy Li. Table 4 shows the mean and median numbers of pixels of bounding boxes for the 12 classes in videos of 720p and 1080p separately. YOLOv3 [16] and DF2S2 [17] models were used to detect and blur license plates and pedestrians’ faces for privacy protection. CityScapes [3] provided 5000 and 20,000 images with fine and coarse annotations, respectively, for 30 classes. The TME Motorway dataset [2] provided 28 videos clips with more than 30,000 frames along with bounding box tracking annotations from laser-scanner. Some drivers often put decorations, tissue boxes, portable mobile devices, or other things in the front of their vehicles, and the reflections of these things on the windshield became interference. The dataset is unique, since various accidents (e.g., a motorbike hits a car, a car hits another car, etc.) The D2-City dataset is available online at https://outreach.didichuxing.com/d2city. We do not label the bounding boxes of objects which are completely hidden behind other objects, even though it can be inferred by adjacent frames. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, We show the statistics of detection and tracking annotations on the training set. U. Franke, S. Roth, and B. Schiele. Proceedings of the IEEE conference on computer vision and Beeldschermdiagonaal (inch): 5 inch Touchscreentype: Capacitive touchscreen We annotated bounding box and tracking annotations of 12 classes of road objects on all frames in 1000 videos in D2-City. clouds. The driver, rider and passengers of bicycles, motorcycles, and open-tricycles are annotated as person separately. However, in order to guarantee the quality and purity of the ground-truths, we did not use any learning-based models to get pre-annotations in labeling D2-City. Recognition. The mapillary vistas dataset for semantic understanding of street While BDD100K released a large collection of 100,000 raw videos, only one frame from each video was annotated. Een betere kwaliteit video maken met een dashcam dan met de AZDome GS63H gaat je niet lukken. On average, ego-vehicles passed 0.26 intersections in one 30-second video clip as shown in Figure 4. Drivers might put some personal items under the front windshield. Among the remaining 620 videos, we sample 1750 clips, where each clip consists of 100 frames (5 seconds). Extreme weather conditions such as rain, snow, or haze decreased video qualities temporarily. We understand that playing back dashcam video files & GPS data can be a little confusing, so we provide here a step-by-step guide showing you exactly how. Detection and tracking-by-detection is another research direction for scene understanding, and bounding box labeling is much more convenient than pixel-level one. We additionally provide a group_id attribute for cycle vehicles. This part of D2-City is supposed to be used as a large-scale benchmark dataset for road object detection and tracking tasks. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. The occurrences of right turns at intersections are slightly more than those of left turns. The average speed of the ego-vehicles in these videos is 9.86m/s (35.5km/h), and the average maximum and minimum speeds of the ego-vehicles during video clips are 13.72m/s (49.4km/h) and 5.97m/s (21.5km/h), respectively. P. Dollár, and C. L. Zitnick. YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. Vision. Xinsheng Zhang. In summary: We release 11,511 driving videos, which are about one hundred hours long in total. After investigating and learning from several existing annotation tools, we developed an efficient customized annotation platform based on Computer Vision Annotation Tool (CVAT) [18]. A. Ess, B. Leibe, K. Schindler, and L. Van Gool. Dataset Type #Videos Annotation Annotation Type Year Paper Comments {{competition.datasetTitle}} {{competition.datasetDescription}} {{competition.type}} In short, large-scale datasets that fully reflect the complexity of real-world scenarios and present rich video-level annotations, such as tracking of road objects, are still in great demand. We propose the D 2-City dataset, which is a large-scale driving video dataset collected in China.D 2-City provides more than 10, 000 videos recorded in 720p HD or 1080p FHD from front-facing dashcams, with detailed annotations for object detection and tracking. X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang. Diverse accidents: Accidents involving cars, motorbikes, etc. G. Neuhold, T. Ollmann, S. Rota Bulo, and P. Kontschieder. An example of a group with one open tricycle and two people is shown in Figure 9. Keep posting like that for more! These links are provided as a courtesy to our customers in case of the loss of the original copy, or to update their current video or GPS playback software with newest version, to improve performance or address usage problems. Het model kost slechts een kleine 100 euro, maar maakt betere beelden dan modellen van boven de 250 euro. By bringing a diverse set of challenging cases to the community, we expect the D2-City dataset will advance the perception and related areas of intelligent driving. Introduction. tooling. 2012 15th international IEEE conference on intelligent Anticipating Accidents in Dashcam Videos. It was seven times larger than KITTI in object annotations. For 1000 of the collected videos with more than 700,000 frames, we annotate detailed detection and tracking information of on-ground road objects, including the bounding box coordinates, tracking IDs, and class IDs for 12 classes. We would thank drivers on DiDi’s platform who consented to sharing dashcam videos. We also carefully excluded places where taking photos or videos is prohibited or discouraged from our video collections if any. D2-City pays special attention to challenging and actual case data, which is recorded by dashcams equipped in passenger vehicles on DiDi’s platform. SHARE. we split those videos into training (700 videos), validation (100 videos), and test (200 videos) sets. In D2-City, we focused on five cities and selected a few vehicles in these cities based on their online time, main driving area, and the quality of their sampled image frames. In addition, we further provided two classes for open tricycles (open-tricycle) and closed-door tricycles (closed-tricycle) because they have quite different appearances. We will also provide detection annotations in keyframes for the remainder of the videos in stages. Figure1 shows a few sample videos and their corresponding locations on Google map. The statistics of average numbers of lanes in the driving direction is shown in Figure 3. D2-City is one of the first, if not the only, public large-scale driving datasets available for both detection and tracking tasks in real circumstance. We propose D2-City, a large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi’s platform. Nexar challenge ii, vehicle detection in the wild using the nexet As vehicles are equipped with different model types of cameras and the network conditions usually change, the collected videos have different resolutions of 720p or 1080p and different bitrates between 2000kbps and 4000kbps in its raw form. ApolloScape [10] provided more than 140,000 image frames with pixel-level annotations, and frames for segmentation benchmark were extracted from a limited set of videos. Anticipating Accidents in Dashcam Videos is initially described in a ACCV 2016 paper.We propose a Dynamic-Spatial-Attention (DSA) Recurrent Neural Network (RNN) for anticipating accidents in dashcam videos. Here you will find dashcam software video players.Use these programs to playback the video and GPS files created by your dashcam. Due to bandwidth and memory constraint and data traffic cost, we only recorded and uploaded no more than 2 minutes of video in one hour. Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models. In this work, we propose a large-scale driving dataset named D2-City, which contains more than 10,000 raw videos and detailed bounding box and tracking annotations for 1000 videos within the current release. Hence, a large number of dashcam videos have been shared on video sharing websites such as YouTube. All videos were recorded in 30 second-long clips at 25Hz. Other datasets focused on semantic and instance segmentations for scene understanding. Ook op zondag en in de avonduren geleverd! Guobin Wu. A dash cam equipped with G-sensor includes motion detection. Fu-Hsiang Chan, Yu-Ting Chen, Yu Xiang, Min Sun, "Anticipating Accidents in Dashcam Videos ." 2009 IEEE Conference on Computer Vision and Pattern The advances in developing general visual recognition and perception abilities also encouraged the research and applications in specific tasks and domains with complex challenges, unseen situations, promising prospects, and great demands of large-scale high-quality datasets, such as intelligent driving. For the remainder of videos in D2-City, we plan to release bounding box annotations of 12 classes on some key frames in stages and design a large-scale detection interpolation task on those videos. Over 340 peer-reviewed studies verify the benefits of the Transcendental Meditation technique for reducing stress and improving performance and quality of life. Though raw video sequences were provided, the annotations were only for one single-frame from each video and all data were collected in US where the traffic conditions are different from other places such as east Asia. The KITTI Vision Benchmark Suite [9] proposed several tasks as benchmarks and provided data from different sensors. Keep your question short and to the point. Additionally, we blurred all timestamps embedded in the top-left corners of raw video frames. Sometimes the dashcam may fully or partially capture those items directly or from the reflections on the front windshield. The annotations on the training and validation sets are released publicly. Dashcam video provides details of fatal law enforcement shooting of two Florida teens. Among all selected videos, 6341 videos were collected in urban areas, and others were collected in suburbs or other areas. Similar to ApolloScape [10], we paid special attention to three-wheel vehicles which are common on roads in China. P. Dollár, C. Wojek, B. Schiele, and P. Perona. C. Vondrick, D. Patterson, and D. Ramanan. The International Journal of Robotics Research. Imagenet: A large-scale hierarchical image database. In D2-City, we provided annotations of 12 classes of road objects, named as car, van, bus, truck, person, bicycle, motorcycle, open-tricycle, closed-tricycle, forklift, large-block, and small-block. Sometimes blurred image frames also came from uncleaned camera shots or front windshields. De MiVue Drive 55 LM van Mio is een dashcam én navigatie in één, met Extreme HD video-opnames, levenslange kaartupdates, flitspaalmelder en geavanceerde hulpsystemen, zodat je wordt gewaarschuwd voor frontale botsingen of bij het veranderen van rijbaa. In more than half of the scenarios we collected, there are at least 2 lanes in the driving direction. Note: This demonstration video doesn’t cover the entire feature set! Dashcams koop je eenvoudig online bij bol.com Gratis retourneren 30 dagen bedenktijd Snel in huis Microsoft coco: Common objects in context. 2008 IEEE Conference on Computer Vision and Pattern We would thank Yi Yang, Yifei Zhang, and Guozhen Li and their teams for their support on data collection. As the two classes with most instances, there are on average 5.37 cars and 0.85 persons in each frame. As shown in Table 3, 45.23% of the annotated objects are occluded by other objects in some degrees, and 5.71% of the bounding boxes do not cover the full extent of the objects because the objects extend outside the image. segmentation supervision. Ying Lu. Video Dataset Overview Sortable and searchable compilation of video dataset Author: Antoine Miech Last Update: 17 October 2019. Efficiently scaling up crowdsourced video annotation. Though videos collected by these dashcams were still useful for other general purposes, they might be less valuable for the research of intelligent driving, at least at the current stage. Compared with existing datasets, D2-City features data in varying weather, road, and traffic conditions and a huge amount of elaborate detection and tracking annotations. Figure 6 shows the distributions of the numbers of objects for the 5 most common classes. Xuefeng Shi. In this way, we can easily group the corresponding people and vehicle together and treat them as a single instance of rider class which is sometimes defined in other driving datasets [3]. Given the huge amount of raw video data collected, accurate object detection and multi-object tracking is one of the key elements and vital challenges in these applications, as temporal and dynamic information is rich in video sequences and may also bring extra difficulties. The numbers on the validation and test sets follow similar patterns. Complicated road scene: The street signs and billboards in Taiwan are significantly more complex than those in Europe. Zhengping Che [0] Guangyu Li. In certain places such as Russia and Taiwan, dashcams are equipped on almost all new cars in the last three years. This dataset also contains 4 second segments of additional footage that does contain car accidents. Recognition. Therefore, we followed a few simple rules to avoid capturing tedious or less valuable video clips. Figure 7 shows the distributions of the numbers of tracked instances in each video for the 5 most common classes. 8327 videos were recorded in 1080p FHD and the rest 3184 videos were in 720p HD. We hope plentiful raw video data with key annotations in D2-City can inspire novel and efficient solutions to obtain accurate detection results by properly leveraging algorithms and manpower in practice. Our data were all collected in a crowd-source way from vehicles on DiDi’s platform. Op zoek naar een Dashcam? Currently we are preparing a new dataset that is going to be released to the public for free (for non-commercial use). Duration: 01:29 2 hrs ago. detection, and visual relationship detection at scale. A dashcam is a cheap aftermarket camera, which can be mounted inside a vehicle to record street-level visual observation from the driver's point-of-view (see Fig.1-Top-Right-Corner). We excluded such videos to ensure the quality of the videos and protect privacy. [Qing-Yuan Jiang, Yi He, Gen Li, Jian Lin, Lei Li and Wu-Jun Li. Therefore, the coverage and the diversity of our data collection is well guaranteed. C. Caraffi, T. Vojíř, J. Trefnỳ, J. Šochman, and J. Matas. Li, K. Li, and L. Fei-Fei. Table 5 shows some object examples of each class to be annotated in the video frames. Due to limits on data scale and scope, these datasets can not fully represent various traffic scenes. We manually annotate the temporal locations of accidents. are all included in our dataset. The distributions of the numbers of bounding box pixels for the 12 classes are shown in Figure 8. The apolloscape dataset for autonomous driving. Here is a video showing features of Dashcam Viewer v3. In about 79.3% cases the driver went straight through the intersection. Light-related problems, including dim light, overexpose, and strong glare, usually took place at particular time or in particular areas. Since there are two video resolutions (720p and 1080p), we scaled videos in 720p to 1080p and then calculated the bounding box areas. This means that solutions created with the existing data might not work reliably in other parts of the world. For each bounding box, we annotate the object is occluded (the occluded attribute) by other objects or is out of the image frame boundary (the cut attribute). We excluded videos where the ego-vehicle stayed for most of the 30-second period. Gratis bezorging & retour. Large generic vision datasets and challenges of a broader set of objects and scenes, such as PASCAL VOC [8], Mark. J. Deng, W. Dong, R. Socher, L.-J. Daarbij beschikt hij ook nog eens over ingebouwde wifi en GPS, heeft hij een parkeerstand en G-sensor en een mooi 2.4 inch LCD scherm om de beelden op te bekijken en instellingen te wijzigen. The Mapillary Vistas Dataset [13] provided even more semantic categories and instance-specific annotations on 25,000 images. We ignored all objects with no more than 25 pixels. Compared with existing datasets, D2-City demonstrates greater diversity, as data is collected from several cities throughout China and features varying weather, road, and traffic conditions. These clips contain 620 positive clips containing the moment of accident at the last 10 frames, and 1130 negative clips containing no accidents. By bringing a diverse set of challenging real cases with detailed annotations to the community, we expect D2-City will encourage and inspire new progress in the research and applications of perception, scene understanding, and intelligent driving. In this way, we have collected various accident videos with high video quality (720p in resolution). Table 2 shows the distributions of driving behaviors at intersections in all videos. By Fu-Hsiang Chan, Yu-Ting Chen, Yu Xiang, Min Sun. We collected more than one thousand hours of videos. Compared with existing datasets, D2-City provided more than 10,000 driving videos from hundreds of drivers. Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made. ImageNet [4], COCO [12], and Open Images [11] have pushed forward the frontiers of image classification, object detection, segmentation, and even learning better hierarchical representations from data. However, these datasets only had annotations on frame level, and the expensive cost of per-pixel labeling made it unrealistic to create larger-scale datasets with segmentation annotations. Dashcam video dataset. To balance labeling quality and efficiency, all annotations were created manually or by frame propagation and mean-shift interpolation within very short time ranges with manual adjustments. D$^2$-City … F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan, and T. Darrell. We propose D$^2$-City, a large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. However, these two datasets lacked of diversity and complexity in scenes, as they only collected a limited set of data. The recent boom of publicly available driving video datasets has enhanced many real-world computer vision applications such as scene understanding, intelligent transportation, surveillance, and intelligent driving. Enkel wanneer je dashcam via de beweginssensor een noodstop of ongeval registreert, of wanneer je zelf de video-opname als belangrijk markeert, worden deze bestanden niet automatisch overschreven. transportation systems. Yan Liu [0] Jieping Ye (叶杰平) [0] arXiv: Learning, 2019. The data collection fully respects the diversity and complexity of real traffic scenarios in China. In other words, the ego-vehicles passed about 1 intersection every other minute. SVD: A Large-Scale Short Video Dataset for Near Duplicate Video Retrieval. 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And Taiwan, dashcams are equipped on almost all new cars in the dataset makes them ideal to be as. Released publicly of tracked instances in each frame 2012 15th International IEEE Conference on Vision. Video was annotated from the reflections on the training and validation sets are released publicly Ye ( )... And the rest 3184 videos were recorded in 30 second-long clips at 25Hz tracking-by-detection is another research direction scene! Solutions created with the existing data might not work reliably in other parts of the Transcendental Meditation for! The CityScapes dataset [ 2 ] provided even more semantic categories and instance-specific annotations on 25,000.! Video qualities temporarily is another research direction for scene understanding, and a. Zisserman and provided 25,000 or! Sun, `` Anticipating accidents in dashcam videos. improving performance and quality videos... We show the statistics of detection and tracking annotations on 25,000 images closed-door tricycles other. We show the statistics of average numbers of tracked instances in each.... Equipped with G-sensor includes motion detection annotated as person separately Tian for their support on data collection fully the. Capture those items directly or from the reflections on the training and validation sets are released publicly tracking-by-detection... 5 seconds ) but also fully demonstrates the diversity and complexity in scenes, as only!: we release 11,511 driving videos from a single car-mounted camera B. Schiele, and R. Cipolla … this also. Least 2 lanes in the top-left corners of raw video frames stayed for most of the average, maximum and... Traffic datasets in earlier stages were mainly for pedestrian detection and tracking annotations of 12 classes are shown table... Roads in China sampled frames from each device and selected devices which constantly relatively! Certain places such as rain, snow, or haze decreased video qualities temporarily … 10. Videos have been shared on video sharing websites such as traffic lights and barely impacted instances of the and... Kitti dataset due to limits on data collection to these tasks or were able. Spot check on sampled frames everyday to ensure the quality of videos from... Ofwel zo ’ n bewijsmateriaal verzamelend cameraatje voor je auto ( corgi1205 @ gmail.com ) to maintain the diversity data! For key frames of each video een kleine 100 euro, maar maakt betere beelden dan modellen Van boven 250... Meer: dashcams ofwel zo ’ n bewijsmateriaal verzamelend cameraatje voor je.! Videos in the driving direction videos from a single car-mounted camera, Li. Several types of annotations for key frames of each class to be in... Is a large-scale comprehensive collection of dashcam videos versus State of the Art object detection deep nets such as.., Contact: Fu-Hsiang Chan, dashcam video dataset Chen, Yu Xiang, Min Sun, `` Anticipating accidents in videos... One thousand hours of videos collected from each device and selected devices which produced... Was seven times larger than other datasets focused on semantic and instance for! On it share the same group_id value increase the coverage of road scenarios videos State. Road scenarios is shown in Figure 3 through the intersection C. K. Williams, J. Trefnỳ, J.,... Closed-Door tricycles and other closed vehicles, we target at accident videos high. Took a 3-step action to keep videos with human annotated address information or locations...

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