An abandoned object detection algorithm based on improved GMM and short-term stability measure

Traditional abandoned object detection algorithms encounter the problems of high computational complexity and poor environmental adaptability. This paper proposes an abandoned object detection algorithm based on improved Gaussian Mixture Modeling (GMM). The matching result of foreground model is considered and the short-term stability measure is employed to make a compound judgment. The information of abandoned objects contained in foreground models and the pixel-level objects status information is deeply explored to reduce the risk that foregrounds model changes into background model. The working principles of foreground model and short-term stability measure are analyzed in detail and the concrete algorithm flow is given. The proposed algorithm keeps the advantages of traditional GMM, meanwhile the foreground model and short-time stability measure enable the proposed algorithm to detect abandoned objects precisely. The experimental results under different conditions demonstrate that the proposed algorithm has better environmental adaptability and it detects much less wrong blobs than the other algorithm. The proposed algorithm achieves a better performance under complex background condition.