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.

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

In computer vision, the multiple objects tracking play a vital challenging role. To solve the issues in this research field, various traditional techniques had been developed. In this paper, we consider the problem of tracking multiple persons in a dynamic environment (background) such as illumination changes and shadow moving. Notably, i) Estimating camera motion and ii) Multiple persons tracking are the two main phases involved in our proposed approach. In the first phase, the good features were extracted using both the SIFT features extraction steps and Gaussian noise elimination method. Instead of using the conventional SIFT-based matching method, we have introduced a new fuzzy matching method to create an adaptive matching zone (region). Using this, the two corresponding features from different frames can be matched perfectly. The brightness of a matching feature of interest indicates its size. Additionally, we use the magnitude and direction of the motion for accurate elimination of camera motion. In the second phase, the persons are tracked from the moving object by finding the optimal feature points and clustering of final points are made as the moving persons (objects). Experimental validation was performed on different challenging datasets and promising results are achieved by our proposed method compared to other existing methods.

Saravanan Palaniswamy

Papers

Efficient fuzzy feature matching and optimal feature points for multiple objects tracking in fixed and active camera models