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Towards Understanding and Inferring the Crowd: Guided Second Order Attention Networks and Re-identification for Multi-object Tracking
- Source :
- IROS
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Multi-human tracking in the crowded environment is a challenging problem due to occlusions, pose change, viewpoint variation and cluttered background. In this work, we propose a robust feature learning for tracking-by-detection methods based on second-order attention network that can capture higher-order relationships between salient features at the early stages of Convolutional Neural Network (CNN). Guided Second-Order Attention Network (GSAN) that, unlike the existing attention learning methods which are weakly-supervised, uses a supervisory signal based on the quality of the self-learned attention maps. More specifically, GSAN looks into the attended maps of a person having the highest confidence and supervise itself to look into the correct regions in the images of the person. Attention maps learned this way are spatially aligned and thus robust to camera-view changes and body pose variations. We verify the effectiveness of our approach by comparing with the state-of-the-art methods on challenging person re-identification and multi object tracking (MOT) datasets.
- Subjects :
- business.industry
Computer science
SIGNAL (programming language)
020206 networking & telecommunications
02 engineering and technology
Variation (game tree)
Tracking (particle physics)
Convolutional neural network
Salient
Video tracking
0202 electrical engineering, electronic engineering, information engineering
Clutter
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
business
Feature learning
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Accession number :
- edsair.doi...........4684fb6e33074556664540fca93e9654