Gaussian Mixture Model Background Subtraction Research Articles

The Inheritance and Development of Ethnic Folk Dance in Dance Teaching under the Background of Informatization

Abstract With the arrival of the informationization era, the inheritance and development of ethnic folk dance in dance teaching faces new challenges and opportunities. This paper discusses effectively inheriting and developing ethnic folk dance under informationization. The research methodology includes the Analysis of folk dance movement characteristics, movement capture and feature extraction based on Kinect acquisition technology. The results show that the dancer’s movement data can be accurately recorded by Kinect capture technology, and the error of capture coordinates can be minimized up to 1.115. The study also finds that the dance movement features can be effectively extracted using Gaussian Mixture Model Background Subtraction and Median Filtering preprocessing techniques. In addition, the “Trinity Teaching Assistive Technology” proposed in this study can improve teaching quality. The application of information technology not only promotes the scientific and standardized teaching of folk dance, but also helps improve students’ skills and creativity.

Efficient parallel implementation of Gaussian Mixture Model background subtraction algorithm on an embedded multi-core Digital Signal Processor

Scene segmentation is crucial in video processing applications, such as vehicle detection. The Gaussian Mixture Model (GMM) based background subtraction (BS) is a widely employed technique for scene segmentation. However, due to its high computational demands, an efficient parallel implementation is necessary. This paper proposes a parallelized implementation of the GMM algorithm on the C6678 digital signal processor (DSP), utilizing its eight cores. Three different parallelization approaches are presented: Open Multiprocessing (OpenMP), manually optimized method, and manually optimized method involving Direct Memory Access (DMA) engine. To evaluate their effectiveness, a metrics comparison is conducted using background truth images provided by the literature dataset references. The experimental results demonstrate that the proposed manually optimized method surpasses the current state-of-the-art. It achieves a remarkable parallel efficiency of approximately 99% for low and medium frame sizes and 83% and 79% for high-definition (HD) and full-HD resolution frames, respectively, when all eight cores are enabled. In contrast, the parallel efficiency obtained using OpenMP is only 45%.

Vehicle Detection in a Video Frame Using Machine Learning Technology

Motion tracking is one of the most active research titles of the computer vision concepts. Vehicle detection process on road are used for vehicle tracking, counts, traffic analysis and vehicle categorizing objectives and it can be implemented under different environments changes. Vehicle counting process provides appropriate information about traffic flow, and the vehicle crash occurrences and also traffic peak times in roadways[2]. Analysis of traffic may account traffic and also streamline the process, Analysis of traffic may account for the number of vehicles in an area per some time period[1]. Most of the design involve use of sensors to detect the vehicles. As it is a detection of vehicle in a video frame it uses the algorithms like Gaussian Mixture Model background subtraction it is a widely used approach for foreground detection. By the digital image processing methods which including object detection, edge detection, frame differentiation and kalman filter using the different library and algorithm with real time image[2]. This paper describes the detection of vehicle in a video frame, tracking in the video frame, counts the number of vehicle passes through the indicative line in roadways.

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

In recent years, background subtraction techniques have been used in vision and image applications for moving target detection. However, most methods cannot provide fine results due to dynamic backgrounds, noise, etc. The Gaussian mixture model (GMM) is a background modeling method commonly used in moving target detection. The traditional GMM method is vulnerable to noise interference, especially from dynamic backgrounds; thus, its detection performance is not good. Because of the influence of background noise and dynamic effects on moving target detection, we propose a method of moving target detection for dynamic backgrounds based on improved GMM background subtraction. This method can be divided into three stages. First, in the background modeling stage, to facilitate calculation and improve modeling speed, the video frame is blocked, and the background model is reconstructed using the image block averaging method. Second, in the moving target detection stage, the method of combining wavelet semi-threshold function denoising with mathematical morphology closed operation is used for denoising, which effectively eliminates the influence of noise and improves the detection effect. Third, in the background updating stage, the adaptive background updating method is used to update the background to improve detection results. The simulation results show that the improved method can reduce noise and dynamic background interference while improving moving target detection, thereby proving the effectiveness and adaptability of the proposed method.

Towards a better understanding of the overall health impact of the game of squash: automatic and high-resolution motion analysis from a single camera view

Abstract In this paper, we present a method for locating and tracking players in the game of squash using Gaussian mixture model background subtraction and agglomerative contour clustering from a calibrated single camera view. Furthermore, we describe a method for player re-identification after near total occlusion, based on stored color- and region-descriptors. For camera calibration, no additional pattern is needed, as the squash court itself can serve as a 3D calibration object. In order to exclude non-rally situations from motion analysis, we further classify each video frame into game phases using a multilayer perceptron. By considering a player’s position as well as the current game phase we are able to visualize player-individual motion patterns expressed as court coverage using pseudo colored heat-maps. In total, we analyzed two matches (six games, 1:28h) of high quality commercial videos used in sports broadcasting and compute high resolution (1cm per pixel) heat-maps. 130184 manually labeled frames (game phases and player identification) show an identification correctness of 79.28±8.99% (mean±std). Game phase classification is correct in 60.87±7.62% and the heat-map visualization correctness is 72.47±7.27%.

A Real-Time Active Pedestrian Tracking System Inspired by the Human Visual System

Pedestrian detection and tracking play a significant role in surveillance. Despite the numerous detection and tracking methods proposed in the literature, when the pedestrian is too small to recognize, which is a common case in modern surveillance systems, all methods fail. In order to deal with such case, we propose an active pedestrian tracking system inspired by the human visual system. A coarse-to-fine pedestrian detection algorithm is proposed for the small pedestrian detection by combining the Gaussian mixture model background subtraction with the histogram of oriented gradient detection. In addition, a three-dimensional pan–tilt–zoom control model is presented, which requires no calibration and is more accurate than other control models. In order to actively track a pedestrian in real time, we utilize an active control algorithm and a tracking–learning–detection tracker. Experimental results demonstrate that our active tracking system is both efficient and effective.