Object Tracking over a Multiple-Camera Network

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.