A kernel support vector machine based anomaly detection using spatio-temporal motion pattern models in extremely crowded scenes

Millions of security cameras were placed in public spaces, generating large quantities of video data. There is a need to develop smart techniques to identify and classify objects tracking instantly. Most of them concentrate on spatial information, resulting in exposure to noise and background movement. In addition, monitoring individuals in overcrowded scenes is a difficult task, due to the variation of movement and appearance created by the large amount of people in the scene. In this paper, initially, utilizing threshold value, the video is split into frames. Then segmentation of moving objects using Extended Kalman Filters (EKF) to improve the accuracy of the classification. Instead, to distinguish between the foreground object and the background object, the texture features is removed. The artifacts are then labelled using improved Learning Vector Quantization (LVQ) for efficient identification of anomalies. Also an effective classification of Kernel Support Vector Machine (KSVM) predicated on anomaly detection has been suggested utilizing spatio-temporal movement pattern models in overcrowded scenes to solve these problems. Hence, KSVM is more advantageous in accuracy which is used to monitor the object. The result shows the performance of the proposed KSVM obtained high performance compared with SVM and Hidden Markov Model (HMM).