Multi object Tracking using Gradient-based Learning Model in Video-Surveillance

2021 ◽  
Vol 9 (4) ◽  
pp. 0-0
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Learning Model ◽  
Tracking Task ◽  
The Other ◽  
Prediction Algorithm ◽  
Normalization Procedure ◽  
Illumination Changes ◽  
Gradient Based ◽  
Clustering Approach

On accomplishing an efficacious object tracking, the activity of an object concerned becomes notified in a forthright manner. An accurate form of object tracking task necessitates a robust object tracking procedures irrespective of hardware assistance. On the other hand, the tracking gets affected owing to the existence of varied quality diminishing factors such as occlusion, illumination changes, shadows etc novel background normalization procedure articulated on the basis of a textural pattern is proposed in this paper. Environmental Succession Prediction algorithm for discriminating disparate background environment by background clustering approach. Probability based Gradient Pattern (PGP) approach for recognizing the similarity between patterns obtained so far. Comparison between standardized frame obtained in prior and those processed patterns detects the motion exposed by an object and the object concerned gets identified within a blob.

An Efficient Multiple Description Coding for Multi-View Video Based on the Correlation of Spatial Polyphase Transformed Subsequences

2019 ◽  
Vol 63 (5) ◽  
pp. 50401-1-50401-7 ◽  
Author(s):  
Jing Chen ◽  
Jie Liao ◽  
Huanqiang Zeng ◽  
Canhui Cai ◽  
Kai-Kuang Ma
Keyword(s):  
Video Transmission ◽  
Video Sequence ◽  
Three Dimensional ◽  
The Other ◽  
Multiple Description Coding ◽  
Multiple Description ◽  
Prediction Mode ◽  
Gradient Based ◽  
Directional Interpolation ◽  
Description Coding

Abstract For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.

scholarly journals SHIP CLASSIFICATION FROM MULTISPECTRAL VIDEOS

2012 ◽  
Vol 31 (2) ◽  
pp. 121
Author(s):  
Frederique Robert-Inacio ◽  
Ghislain Oudinet ◽  
Francois-Marie Colonna
Keyword(s):  
Video Surveillance ◽  
The Other ◽  
Terrorist Attacks ◽  
Classification Result ◽  
Radio Communications ◽  
Similarity Degree ◽  
Sensitive Areas ◽  
Parameter Measuring ◽  
Ship Classification ◽  
The One

Surveillance of a seaport can be achieved by different means: radar, sonar, cameras, radio communications and so on. Such a surveillance aims, on the one hand, to manage cargo and tanker traffic, and, on the other hand, to prevent terrorist attacks in sensitive areas. In this paper an application to video-surveillance of a seaport entrance is presented, and more particularly, the different steps enabling to classify mobile shapes. This classification is based on a parameter measuring the similarity degree between the shape under study and a set of reference shapes. The classification result describes the considered mobile in terms of shape and speed.

scholarly journals Image Gradient-based Joint Direct Visual Odometry for Stereo Camera

2017 ◽  
Author(s):  
Jianke Zhu
Keyword(s):  
Visual Odometry ◽  
Research Problem ◽  
Feature Representation ◽  
Local Optimum ◽  
Multi Scale ◽  
Illumination Changes ◽  
Pyramid Scheme ◽  
Gradient Based ◽  
Feature Based ◽  
Dense 3D Reconstruction

Visual odometry is an important research problem for computer vision and robotics. In general, the feature-based visual odometry methods heavily rely on the accurate correspondences between local salient points, while the direct approaches could make full use of whole image and perform dense 3D reconstruction simultaneously. However, the direct visual odometry usually suffers from the drawback of getting stuck at local optimum especially with large displacement, which may lead to the inferior results. To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose. The key of our approach is a dual Jacobian optimization that is fused into a multi-scale pyramid scheme. Moreover, we introduce a gradient-based feature representation, which enjoys the merit of being robust to illumination changes. Furthermore, a joint direct odometry approach is proposed to incorporate the information from the last frame and previous keyframes. We have conducted the experimental evaluation on the challenging KITTI odometry benchmark, whose promising results show that the proposed algorithm is very effective for stereo visual odometry.

scholarly journals Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

2019 ◽  
Vol 9 (1) ◽  
pp. 6299-6304
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Traffic Monitoring ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Moving Object Tracking

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

scholarly journals Expanding Attentional Capacity with Adaptive Training on a Multiple Object Tracking Task

2011 ◽  
Vol 11 (11) ◽  
pp. 292-292
Author(s):  
T. W. Thompson ◽  
M. L. Waskom ◽  
J. D. E. Gabrieli ◽  
G. A. Alvarez
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Attentional Capacity ◽  
Adaptive Training ◽  
Multiple Object

Motion Segmentation and Matting by Graph Cut

2012 ◽  
pp. 95-117
Author(s):  
Jiangjian Xiao
Keyword(s):  
Video Sequence ◽  
Graph Cut ◽  
The Other ◽  
Robust Approach ◽  
Alpha Matting ◽  
Optical Flow Field ◽  
Segmentation Boundary ◽  
Clustering Approach ◽  
Multiple Frames ◽  
Layer Segmentation

Given a video sequence, obtaining accurate layer segmentation and alpha matting is very important for video representation, analysis, compression, and synthesis. By assuming that a scene can be approximately described by multiple planar or surface regions, this chapter describes a robust approach to automatically detect the region clusters and perform accurate layer segmentation for the scene. The approach starts from optical flow field or small corresponding seed regions and applies a clustering approach to estimate the layer number and support regions. Then, it uses graph cut algorithm combined with a general occlusion constraint over multiple frames to solve pixel assignment over multiple frames to obtain more accurate segmentation boundary and identify the occluded pixels. For the non-textured ambiguous regions, an alpha matting technique is further used to refine the segmentation and resolve the ambiguities by determining proper alpha values for the foreground and background, respectively. Based on the alpha mattes, the foreground object can be transferred into the other video sequence to generate a virtual video. The author’s experiments show that the proposed approach is effective and robust for both the challenging real and synthetic sequences.

Complementary Object Tracking Using Average Peak-to-Correlation Energy

2021 ◽  
Author(s):  
Kosuke Honda ◽  
Hamido Fujita
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Correlation Energy ◽  
Target Object ◽  
The Other ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Evaluation Index ◽  
Siamese Network

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.

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