H.-M. Hsu

H.-M. Hsu

ECE @ UWashington

Publications

Multi-View Vehicle Re-Identification using Temporal Attention Model and Metadata Re-ranking.

Published in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019

Multi-View Vehicle Re-Identification using Temporal Attention Model and Metadata Re-ranking.

Object re-identification (ReID) is an arduous task which requires matching an object across different non-overlapping camera views. Recently, many researchers are working on person ReID by taking advantages of appearance, human pose, temporal constraints, etc. However, vehicle ReID is even more challenging because vehicles have fewer discriminant features than human due to viewpoint orientation, changes in lighting condition and inter-class similarity. In this paper, we propose a viewpoint-aware temporal attention model for vehicle ReID utilizing deep learning features extracted from consecutive frames with vehicle orientation and metadata attributes (i.e., type, brand, color) being taken into consideration. In addition, re-ranking with soft decision boundary is applied as post-processing for result refinement. The proposed method is evaluated on the CVPR AI City Challenge 2019 dataset, achieving mAP of 79.17% with the second place ranking in the competition.

Recommended citation: Huang, T. W., Cai, J., Yang, H., Hsu, H. M., & Hwang, J. N. (2019, June). Multi-View Vehicle Re-Identification using Temporal Attention Model and Metadata Re-ranking. In CVPR Workshops (Vol. 2). https://openaccess.thecvf.com/content_CVPRW_2019/papers/AI%20City/Huang_Multi-View_Vehicle_Re-Identification_using_Temporal_Attention_Model_and_Metadata_Re-ranking_CVPRW_2019_paper.pdf

Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models

Published in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019

Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models

Due to the exponential grow of traffic camera networks, the need of multi-camera tracking (MCT) for intelligent transportation has received more and more attentions. The challenges of MCT include similar vehicle models, large feature variation in different orientations, color variation of the same car due to lighting conditions, small object sizes and frequent occlusion, as well as the varied resolutions of videos. In this work, we propose an MCT system, which combines single-camera tracking (SCT), deep feature re-identification and camera link models for inter-camera tracking (ICT). For SCT, we use a TrackletNet Tracker (TNT) , which effectively generates the moving trajectories of all detected vehicles by exploiting temporal and appearance information of multiple tracklets that are created by associating bounding boxes of detected vehicles. The tracklets are generated based on CNN feature matching and intersection-over-union (IOU) in every single-camera view. In terms of deep feature re-identification, we exploit temporal attention model to extract the most discriminant feature of each trajectory. In addition, we propose the trajectory-based camera link models with order constraint to efficiently leverage the spatial and temporal information for ICT. The proposed method is evaluated on CVPR AI City Challenge 2019 City Flow dataset, achieving IDF1 70.59%, which outperforms competing methods.

Recommended citation: Hsu, H. M., Huang, T. W., Wang, G., Cai, J., Lei, Z., & Hwang, J. N. (2019, June). Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models. In CVPR Workshops (pp. 416-424). https://openaccess.thecvf.com/content_CVPRW_2019/papers/AI%20City/Hsu_Multi-Camera_Tracking_of_Vehicles_based_on_Deep_Features_Re-ID_and_CVPRW_2019_paper.pdf