scholarly journals Person Tracking System by Fusing Multicues Based on Patches

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Song Min Jia ◽  
Li Jia Wang ◽  
Xiu Zhi Li ◽  
Lin Feng Wen
Keyword(s):  
Tracking System ◽  
Previous Knowledge ◽  
Complex Environment ◽  
Person Tracking ◽  
Discrimination Ability ◽  
Depth Maps ◽  
Texture Information ◽  
Illumination Changes ◽  
Color Histograms ◽  
The Given

A person tracking algorithm by fusing multicues based on patches is proposed to solve the problem of distinguishing person, occlusion, and illumination variations. Kinect is mounted on the robot for providing color images and depth maps. A detector representing a person by using the fusion of multicues based on patches is proposed. The detector divides the person into many patches and then represents each patch by using depth-color histograms and depth-texture histograms. The appearance representation, considering depth, color, and texture information, has powerful discrimination ability to handle the problems of occlusion, illumination changes, and pose variations. Considering the motion of the robot and person, a tracker called motion extended Kalman filter (MEKF) is presented to predict the person’s position. The result of the tracker is treated as a candidate sample of the detector, and then the result of the detector is the previous knowledge of the tracker. The detector and tracker supplement each other and improve the tracking performance. To drive the robot towards the given person precisely, a fuzzy based intelligent gear control strategy (FZ-IGS) is implemented. Experiments demonstrate that the proposed approach can track a person in a complex environment and have an optimum performance.

Implemented of Images and Sounds Person Tracking System using Directional Volumetric Display

2019 ◽  
pp. 1085
Author(s):  
Mitsuru Baba ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Tracking System ◽  
Person Tracking ◽  
Volumetric Display

An Approach for the Development of Animal Tracking System

2018 ◽  
Vol 8 (1) ◽  
pp. 15-31 ◽  
Author(s):  
N. Manohar ◽  
Y. H. Sharath Kumar ◽  
G. Hemantha Kumar
Keyword(s):  
Video Segmentation ◽  
Tracking System ◽  
Mean Shift ◽  
Computer Assisted ◽  
Residential Areas ◽  
Data Set ◽  
Knn Classifier ◽  
Gabor Features ◽  
Maximum Similarity ◽  
The Given

In this article, the authors propose a system which can identify and track animals. Identification and tracking of animals has got plenty of applications like, avoiding dangerous animal intrusion into residential areas, avoiding animal-vehicle collisions, and behavioral study of animals and so on. Previously, biologists studied videos to detect and identify animals, a time consuming and difficult task. This requires a fully automatic or computer-assisted system to identify and track animals by video. Initially, frames are extracted from the given video. Segmentation is done to the extracted frames using a maximum similarity-based region merging algorithm. Then, the mean shift-based algorithm is used to track the animals. Finally, the animals are classified using Gabor features and a KNN classifier. Experimentation has been conducted on a data set containing more than 150 videos with 15 different classes.

Target Recognition Application of Real-Time Optical Information Processing System

2014 ◽  
Vol 536-537 ◽  
pp. 197-200
Author(s):  
Lan Zhao ◽  
Tao Zeng
Keyword(s):  
Information Processing ◽  
Visual Tracking ◽  
Target Recognition ◽  
Tracking System ◽  
Processing System ◽  
Tracking Algorithm ◽  
Optical Information ◽  
Illumination Changes ◽  
Imaging Surveillance ◽  
Template Tracking

This paper focuses on the visual tracking algorithm in optical imaging surveillance and tracking system. The tracking particle filter framework deemed find sparse representation problem, can effectively overcome the visual tracking algorithm appears in noise, occlusion, background interference and complex situations such as illumination changes. Morphological methods using digital occlusion area is detected to determine whether the date is added to the template tracking results set, thereby updating the control template, to effectively prevent the drift tracking results.

scholarly journals Segmentation of Moving Objects using Numerous Background Subtraction Methods for Surveillance Applications

2020 ◽  
Vol 9 (3) ◽  
pp. 2553-2563
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Tracking System ◽  
Real Life ◽  
Ground Truth ◽  
Computational Time ◽  
Dynamic Background ◽  
Illumination Changes ◽  
Benchmark Datasets ◽  
Surveillance Applications

Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.

Implemented of Images and Sounds Person Tracking System using Directional Volumetric Display

2019 ◽  
pp. 1085
Author(s):  
Mitsuru Baba ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  
Keyword(s):  
Tracking System ◽  
Person Tracking ◽  
Volumetric Display

Missing Person Tracking System

2021 ◽  
pp. 783-790
Author(s):  
Arjun Besra ◽  
Amir Junaid Ahmed ◽  
Sabina Priyadarshini
Keyword(s):  
Tracking System ◽  
Person Tracking
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