Papers › Real-time 3D Deep Multi-Camera Tracking

Real-time 3D Deep Multi-Camera Tracking

26 Mar 2020arXiv:2003.11753archive 2025-07-28

Quanzeng You, Hao Jiang

Tracking a crowd in 3D using multiple RGB cameras is a challenging task. Most previous multi-camera tracking algorithms are designed for offline setting and have high computational complexity. Robust real-time multi-camera 3D tracking is still an unsolved problem. In this work, we propose a novel end-to-end tracking pipeline, Deep Multi-Camera Tracking (DMCT), which achieves reliable real-time multi-camera people tracking. Our DMCT consists of 1) a fast and novel perspective-aware Deep GroudPoint Network, 2) a fusion procedure for ground-plane occupancy heatmap estimation, 3) a novel Deep Glimpse Network for person detection and 4) a fast and accurate online tracker. Our design fully unleashes the power of deep neural network to estimate the "ground point" of each person in each color image, which can be optimized to run efficiently and robustly. Our fusion procedure, glimpse network and tracker merge the results from different views, find people candidates using multiple video frames and then track people on the fused heatmap. Our system achieves the state-of-the-art tracking results while maintaining real-time performance. Apart from evaluation on the challenging WILDTRACK dataset, we also collect two more tracking datasets with high-quality labels from two different environments and camera settings. Our experimental results confirm that our proposed real-time pipeline gives superior results to previous approaches.

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Human DetectionMulti-Object Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Object Tracking Wildtrack DMCT Stack IDF1 81.9 #6 of 9 Archive leaderboard report
Multi-Object Tracking Wildtrack DMCT Stack MOTA 74.6 #6 of 9 Archive leaderboard report
Multi-Object Tracking Wildtrack DMCT IDF1 77.8 #7 of 9 Archive leaderboard report
Multi-Object Tracking Wildtrack DMCT MOTA 72.8 #7 of 9 Archive leaderboard report

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