Moving Human Target Detection and Tracking in Video Frames

Abstract

The conventional method for moving human target detection and tracking has come across a major setback due to various hindering factors such as environmental lighting conditions, temperature, etc. Similarly, it has been noticed that the manual selection of moving human targets in a video sequence does not provide convincing results either. In this paper, a new method for moving human target detection and tracking is proposed. It involves two stages. The first stage consists in the detection of moving human targets and the second one in target tracking based on the Continuously Adaptive Mean-Shift (CAMShift) algorithm. In the first stage, in order to select the moving target, the background subtraction method and frame subtraction method are combined. The Region Of Interest (ROI), which is usually the moving target is identified. In the second stage, target tracking is performed by choosing a centroid pixel point over the ROI, which is then used by the CAMShift algorithm. The proposed method has shown outperforming results for various performance parameters such as precision, accuracy, recall, and the F1-score under three different lighting conditions. The results obtained also show a reduction in time complexity in comparison with the state-of-the-art algorithms.

Arm Movement Analysis Technology of Wushu Competition Image Based on Deep Learning

“…is paper mainly uses the bone model with a total number of joint points of 20. In order to reduce the sensitivity of the bone sequence to the shooting angle, the original bone sequence needs to be transformed into a reference coordinate system determined by the bone sequence itself [23]. e value of the new coordinate system origin can be expressed as…”

IoT Networks Assisted Baduanjin Auxiliary Training System Based on Depth Camera

“…e standard model is adapted to various postures based on the appropriate nonrigid deformation. First, the video is broken into frame sequences by feeding the supplied frame sequence into the model, which outputs the associated posture and form parameters for each frame [10]. e double-layer node graph is calculated according to the predefined body node graph and the initial posture and attitude parameters.…”

Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

“…Scenarios and formulas are given below in Figure 5. This simple method has often been used in combination with other pre-processing methods, such as in [18,19,21]. Displacement of joint at each frame ( −−−−→ P K P K+1 ) is compared with threshold to return boolean value whether it is less or more, through frame depth.…”

“…In addition, there is a simple method for distinguishing whether the target in the current frame is moving/stopped. It is called the "frame subtraction method" [18] and "two-frame difference method" [19]. In this paper, it will be called the adjacent frame subtraction method (AFSM) as well.…”

“…In [19], the authors performed gray variation as pre-processing and subtracted oddeven frames or background-current frames with the AFSM. In [18], the authors used AFSM as a fundamental detection method, but their objective was to find a better threshold value by using canny-edge detection and the Otsu method [20].…”