New mixed adaptive detection algorithm for moving target with big data

• Main Authors: De-gan Zhang, Shan Zhou, Jie Chen, Si Liu
• Format: Article
• Language: English
• Published: JVE International 2016-11-01
• Series: Journal of Vibroengineering
• Subjects: big data; GMM; edge; continuous frame difference; moving target detection
• Online Access: https://www.jvejournals.com/article/17035

Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data.