Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real applications. This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. The dataset consists of 16 scenes captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance, school gate, park, pedestrian mall, and public square. Over 200k video frames are annotated carefully, resulting in more than 3:7 million object bounding boxes and about 7:1 thousand trajectories. We further use this dataset to evaluate the performance of typical algorithms for multiple-object tracking and anomaly behavior analysis and explore the robustness of these methods in urban congested scenarios.
MOT challenge 2015 @cite_20 , challenge 2016 @cite_5 and challenge 2017 @cite_5 are the recent challenging benchmarks for multiple object tracking. The videos in the benchmarks are diverse, and some of which are selected from the existing datasets, KITTI Tracking dataset. However, the dataset consists of various video types including surveillance videos and moving-camera videos. Therefore, it motivates us to establish a public challenging urban surveillance video dataset which is more realistic to evaluate the performance of various algorithms for object tracking and behavior analysis.
@cite_5 @cite_20
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This paper describes the datasets and computer vision challenges that form part of the PETS 2016 workshop. PETS 2016 addresses the application of on-board multi sensor surveillance for protection of mobile critical assets. The sensors (visible and thermal cameras) are mounted on the asset itself and surveillance is performed around the asset. Two datasets are provided: (1) a multi sensor dataset as used for the PETS2014 challenge which addresses protection of trucks (the ARENA Dataset), and (2) a new dataset - the IPATCH Dataset - addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. The dataset specifically addresses several vision challenges set in the PETS 2016 workshop, and corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis ('simple' event detection: the behaviour recognition of a single actor) and High-Level Video Analysis ('complex' event detection: the behaviour and interaction recognition of several actors). In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform. The images and video sequences in the benchmark were captured over various urban suburban areas of 14 different cities across China from north to south. Specifically, VisDrone2018 consists of 263 video clips and 10,209 images (no overlap with video clips) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. With intensive amount of effort, our benchmark has more than 2.5 million annotated instances in 179,264 images video frames. Being the largest such dataset ever published, the benchmark enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. In particular, we design four popular tasks with the benchmark, including object detection in images, object detection in videos, single object tracking, and multi-object tracking. All these tasks are extremely challenging in the proposed dataset due to factors such as occlusion, large scale and pose variation, and fast motion. We hope the benchmark largely boost the research and development in visual analysis on drone platforms.