An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

• Main Authors: Liang Cheng Chang, Shreya Pare, Mahendra Singh Meena, Deepak Jain, Dong Lin Li, Amit Saxena, Mukesh Prasad, Chin Teng Lin
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Big Data and Cognitive Computing
• Subjects: color distribution; particle filter; human tracking; codebook matching; PID controller; GMM
• Online Access: https://www.mdpi.com/2504-2289/4/4/27

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.