Journal article
3D pedestrian tracking and frontal face image capture based on head point detection
Multimedia tools and applications, v 79(1-2), pp 737-764
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper proposes a method to track pedestrians in crowded scenes and capture the close-up frontal face images of a person of interest (POI) for recognition. Pedestrians are tracked via 3D positions of the head points (the highest point of a person) using 2 static overhead cameras. Head points are located and tracked based on the geometric and color cues in the scene. Possible head areas in a frame acquired from one of the overhead cameras are determined based on projective geometry. Head areas belonging to a person are clustered. Without creating a full disparity map of the scene, the 3D position of a pedestrian is obtained by utilizing the disparity along the line segment that passes through his/her head top. The 3D head position is then tracked using common assumptions on motion velocity. If the tracking is not accurate enough, the color distribution of a head top is integrated as a complementary method. With the 3D head point information, a set of pan-tilt-zoom (PTZ) cameras are scheduled to capture the frontal face images of POI. A most suitable PTZ camera is selected by evaluating the capture quality of each PTZ camera and its current state. The approach is tested using a publicly available visual surveillance simulation test bed. The experiments show that the 3D tracking errors are around 4 cm and high quality frontal face images are captured.
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Details
- Title
- 3D pedestrian tracking and frontal face image capture based on head point detection
- Creators
- Zhongchuan Zhang - Applied MaterialsFernand Cohen - Drexel University
- Publication Details
- Multimedia tools and applications, v 79(1-2), pp 737-764
- Publisher
- Springer Nature
- Number of pages
- 28
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000517987600030
- Scopus ID
- 2-s2.0-85073832122
- Other Identifier
- 991019168025404721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic