Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.

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This paper presents an adaptive approach for scale estimation in a tracking-by-detection framework. The proposed method works by learning covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of detection rate compared to region covariance descriptor with using a fixed bounding box (single scale). From this work, it is believed that we have constructed a greater solution in choosing best layer for this descriptor, permitting to move forward to the next issues such as fast motion or motion blur for achieving a robust tracking system.

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