2 code implementations • 21 Nov 2022 • Congliang Li, ShiJie Sun, XiangYu Song, HuanSheng Song, Naveed Akhtar, Ajmal Saeed Mian
Using the labeling method, we provide the KITTI-6DoF dataset with $\sim7. 5$K annotated frames.
3 code implementations • ECCV 2020 • Shi-Jie Sun, Naveed Akhtar, Xiang-Yu Song, HuanSheng Song, Ajmal Mian, Mubarak Shah
Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection. This results in deep models that are detector biased and evaluations that are detector influenced.
1 code implementation • 28 Oct 2018 • Shi-Jie Sun, Naveed Akhtar, HuanSheng Song, Ajmal Mian, Mubarak Shah
In this paper, we harness the power of deep learning for data association in tracking by jointly modelling object appearances and their affinities between different frames in an end-to-end fashion.
1 code implementation • 12 Apr 2018 • Shi-Jie Sun, Naveed Akhtar, HuanSheng Song, Chaoyang Zhang, Jian-Xin Li, Ajmal Mian
A thorough evaluation on PCDS demonstrates that our technique is able to count people in cluttered scenes with high accuracy at 45 fps on a 1. 7 GHz processor, and hence it can be deployed for effective real-time people counting for intelligent transportation systems.