DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes
Tracking in gigapixel scenarios holds numerous potential applications in video surveillance and pedestrian analysis. Existing algorithms attempt to perform tracking in crowded scenes by utilizing multiple cameras or group relationships. However, their performance significantly degrades when confronted with complex interaction and occlusion inherent in gigapixel images. In this paper, we introduce DynamicTrack, a dynamic tracking framework designed to address gigapixel tracking challenges in crowded scenes. In particular, we propose a dynamic detector that utilizes contrastive learning to jointly detect the head and body of pedestrians. Building upon this, we design a dynamic association algorithm that effectively utilizes head and body information for matching purposes. Extensive experiments show that our tracker achieves state-of-the-art performance on widely used tracking benchmarks specifically designed for gigapixel crowded scenes.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, det…
Cell TrackingMulti-Object TrackingObjectObject TrackingJoint Counting, Detection and Re-Identification for Multi-Object Tracking
The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in c…
Multi-Object TrackingMultiple Object TrackingObjectobject-detection+2MOT20: A benchmark for multi object tracking in crowded scenes
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performanc…
Multi-Object TrackingMultiple Object TrackingMultiple Object Tracking with TransformerMultiple People Tracking+2Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-cri…
3D Pedestrian TrackingMultiple Object TrackingObject TrackingJRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking
Autonomous robotic systems operating in human environments must understand their surroundings to make accurate and safe decisions. In crowded human scenes with close-up human-robot interaction and robot navigation, a dee…
DiversityMulti-Person Pose EstimationMulti-Person Pose Estimation and TrackingPose Estimation+2