Title :
Object Tracking over a Multiple-Camera Network
Author :
Zhenzhong Chen ; Weihang Liao ; Bin Xu ; Hongyi Liu ; Qisheng Li ; He Li ; Chao Xiao ; Hang Zhang ; Yiming Li ; Wentao Bao ; Daiqin Yang
Author_Institution :
Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan, China
Abstract :
In this paper, we describe our system for object tracking over a multiple-camera network task in BigMM Challenge in conjunction with the first IEEE International Conference on Multimedia Big Data (BigMM 2015). We focus on the detection and tracking of pedestrians and vehicles. Based on background modeling, we use HOG and SVM to detect pedestrian and morphological processing to detect vehicle in single camera then use spatio-temporal local context for robust object tracking. The features and trajectory of each object in the multiple-camera network are analyzed for matching and camera geometric projection is also employed to optimize the trajectory. We also include the trajectory visualization in our GIS based experiments.
Keywords :
Big Data; automobiles; cameras; feature extraction; geographic information systems; geophysical image processing; image matching; object detection; object tracking; pedestrians; support vector machines; BigMM Challenge; GIS; HOG; IEEE International Conference on Multimedia Big Data; SVM; background modeling; camera geometric projection; image matching; morphological processing; multiple-camera network; object feature analysis; object tracking; object trajectory analysis; pedestrian detection; pedestrian tracking; spatio-temporal local context; trajectory visualization; vehicle detection; vehicle tracking; Cameras; Conferences; Feature extraction; Object tracking; Target tracking; Trajectory; Vehicles; GIS; Object detection; multi-camera network; object tracking; trajectory;
Conference_Titel :
Multimedia Big Data (BigMM), 2015 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-8687-3
DOI :
10.1109/BigMM.2015.53
