Multi Object Tracking Based on Detection with Deep Learning and Hierarchical Clustering
- Resource Type
- Authors
- Hao Zhou; SiFeng Wu; Jun Lu; XueBin Zhao
- Source
- 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC).
- Subject
- Basis (linear algebra)
Computer science
business.industry
Deep learning
Pattern recognition
04 agricultural and veterinary sciences
02 engineering and technology
Frame rate
Tracking (particle physics)
Convolutional neural network
Hierarchical clustering
Video tracking
040103 agronomy & agriculture
0202 electrical engineering, electronic engineering, information engineering
0401 agriculture, forestry, and fisheries
Multi target tracking
020201 artificial intelligence & image processing
Artificial intelligence
business
- Language
Multi object tracking is one of the hotspots of computer vision research. Tracking-by-detection is a common approach to multi-object tracking. With the development of machine learning, especially of deep leaning method, the basis for a tracker becomes much more reliable. The method proposed in this paper is based on detection with convolutional neural network and tracking with hierarchical clustering. Experimental evaluation shows that the proposed method achieves overall competitive performance at high frame rates.