Moving object detection using median-based scale invariant local ternary pattern for video surveillance system

This article presents a novel moving object detection algorithm using median-based scale invariant local ternary pattern for intelligent video surveillance system. Both the texture and color local features are extracted from the incoming frames independently and they are combined at the classification level to improve the object detection results. Here, each incoming image frames are subdivided into several regions and the median-based scale invariant local ternary pattern (MD-SILTP) is obtained for each sub-region. Based on the MD-SILTP patterns, the texture histograms are computed and matched with the background model using the histogram intersection method. Furthermore, the color features are extracted through color histogram matching technique. The background model is then updated based on the best matching texture and color histograms. Finally, the color and texture information are combined for final feature classification. Experiment results illustrate that the fusion of MD-SILTP texture with the color features is stable than the others under smooth surface regions, image noises due to illumination changes, moving cast shadow, and scaling problems.