An Improved Camshift Tracking Algorithm Based on LiDAR Sensor

Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this paper has achieved higher recognition rate and better stability and robustness than the traditional fusion tracking algorithm.

Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.

IMPROVED BIOLOGICAL IMAGE TRACKING ALGORITHM OF ATHLETE’S CERVICAL SPINE HEALTH

ABSTRACT Introduction This paper research an improved biological image tracking algorithm of athlete’s cervical spine health under color feedback. Objective A new algorithm is proposed to improve the accuracy of detection and tracking. Methods In this study, the first thing is to apply the color feedback algorithm to improve and optimize the Improved Camshift algorithm. The optimized algorithm was used to track the center of the image, and the video was processed frame by frame. The center position of the tracking frame was obtained. Results The average number of head twists per person is 39 times. Among the three groups, children twisted the least, and older adults twisted the most. Conclusion The algorithm proposed in this study has certain effectiveness and superiority and can be well applied to detecting the number of head twists during exercise. Level of evidence II; Therapeutic studies - investigation of treatment results.

Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

Face Tracking Performance in Head Gesture Recognition System

This paper describes the comparative analysis of different face tracking methods in the head gesture recognition system. The major constraints of head gesture recognition system, i.e. face detection, feature extraction, tracking, and recognition are explained. We used adaboost algorithm for detection, and Camshift algorithm for tracking with different feature extraction methods. We performed extensive experimentations and presented a comparative analysis of tracking performance of head gesture recognition system under cluttered backgrounds, shadow and sunshine conditions. Experimental results show the robustness in face detection, tracking and direction recognition of the proposed method.