MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance. In the past few years, numerous attempts have been made to perfect the systems. Although they presented favorable performance, they were constrained to track specified category. Drawing on the ideas of few shot method, we pioneered a new multi-target tracking system, named MOTS, which is based on metrics but not limited to track specific category. It contains two stages in series: In the first stage, we design the self-Adaptive-matching module to perform simple targets matching, which can complete 88.76% assignments without sacrificing performance on MOT16 training set. In the second stage, a Fine-match Network was carefully designed for unmatched targets. With a newly built TRACK-REID data-set, the Fine-match Network can perform matching of 31 category targets, even generalizes to unseen categories.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyMulti-Object TrackingMultiple Object TrackingObject TrackingSimilar Papers 제목 키워드 기반
OmniTracker: Unifying Object Tracking by Tracking-with-Detection
Object tracking (OT) aims to estimate the positions of target objects in a video sequence. Depending on whether the initial states of target objects are specified by provided annotations in the first frame or the categor…
Multi-Object TrackingObjectObject TrackingReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attributed to that appearance features are no…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple Object TrackingObject+1Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt convolutions for feature extraction. However, as affected by the inherent receptive field, convolution bas…
Multi-Object TrackingMulti-Object Tracking and SegmentationObject TrackingOnline Multi-Object TrackingSearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aw…
Multiple Object TrackingObjectObject TrackingCML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, h…
Autonomous DrivingInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+7