Performance comparison of object tracking methods on video frames using parallel computing

2014 ◽  
Author(s):  
R Rohit ◽  
S Sachin ◽  
Sharath Prasanna ◽  
R Vignesh ◽  
G Shobha ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Object Tracking ◽  
Performance Comparison ◽  
Video Frames

scholarly journals Fast Evaluation of Segmentation Quality with Parallel Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Henry Cruz ◽  
Martina Eckert ◽  
Juan M. Meneses ◽  
J. F. Martínez
Keyword(s):  
Parallel Computing ◽  
Detection System ◽  
Performance Comparison ◽  
Detection Accuracy ◽  
Fast Evaluation ◽  
Sequential Processing ◽  
Positive Side ◽  
Segmentation Quality ◽  
Automatic Detection System ◽  
The One

In digital image processing and computer vision, a fairly frequent task is the performance comparison of different algorithms on enormous image databases. This task is usually time-consuming and tedious, such that any kind of tool to simplify this work is welcome. To achieve an efficient and more practical handling of a normally tedious evaluation, we implemented the automatic detection system, with the help of MATLAB®’s Parallel Computing Toolbox™. The key parts of the system have been parallelized to achieve simultaneous execution and analysis of segmentation algorithms on the one hand and the evaluation of detection accuracy for the nonforested regions, such as a study case, on the other hand. As a positive side effect, CPU usage was reduced and processing time was significantly decreased by 68.54% compared to sequential processing (i.e., executing the system with each algorithm one by one).

PARALLEL PREDICTOR-CORRECTOR METHODS FOR THE SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS II

1999 ◽  
Vol 10 (01) ◽  
pp. 135-145
Author(s):  
A. M. MAZZONE
Keyword(s):  
Parallel Computing ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Multistep Methods ◽  
Performance Comparison ◽  
Molecular Dynamics Calculations ◽  
The Subject ◽  
The Stability ◽  
Predictor Corrector ◽  
Serial Algorithms

This work presents parallel multistep methods for the solution of ordinary differential equations. The characteristic of parallel computing is that there is a "front" and the computation at points ahead of the front depends only on information behind it. This requires a resetting of serial algorithms and may also lead to numerical errors and instabilities. The analysis of positive and negative aspects of parallel computing is the subject of this paper. Some of the methods presented below are uncommon in the literature on mathematical computing. Others have been elaborated for this study on the basis of the traditional Adams-Bashforth multistep methods. A performance comparison of the methods is made by numerical testing in molecular dynamics calculations. The increase of the number of processors m appears to seriously deteriorate the stability of the calculations and the use of m larger than 2 seems impractical.

Multiple Object Tracking using YOLO-based Detector

2021 ◽  
Author(s):  
Shinfeng D. Lin ◽  
Tingyu Chang ◽  
Wensheng Chen
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Multiple Object Tracking ◽  
Motion Prediction ◽  
Multiple Object ◽  
Bounding Box ◽  
Video Frames ◽  
Tracking Process ◽  
Tracking By Detection ◽  
Bounding Boxes

In computer vision, multiple object tracking (MOT) plays a crucial role in solving many important issues. A common approach of MOT is tracking by detection. Tracking by detection includes occlusions, motion prediction, and object re-identification. From the video frames, a set of detections is extracted for leading the tracking process. These detections are usually associated together for assigning the same identifications to bounding boxes holding the same target. In this article, MOT using YOLO-based detector is proposed. The authors’ method includes object detection, bounding box regression, and bounding box association. First, the YOLOv3 is exploited to be an object detector. The bounding box regression and association is then utilized to forecast the object’s position. To justify their method, two open object tracking benchmarks, 2D MOT2015 and MOT16, were used. Experimental results demonstrate that our method is comparable to several state-of-the-art tracking methods, especially in the impressive results of MOT accuracy and correctly identified detections.

scholarly journals Single-Object Tracking Algorithm Based on Two-Step Spatiotemporal Deep Feature Fusion in a Complex Surveillance Scenario

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyan Chen ◽  
Rui Sheng
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Feature Fusion ◽  
Engineering Application ◽  
Tracking Algorithm ◽  
Single Object ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Deep Feature ◽  
Active Research

Object tracking has been one of the most active research directions in the field of computer vision. In this paper, an effective single-object tracking algorithm based on two-step spatiotemporal feature fusion is proposed, which combines deep learning detection with the kernelized correlation filtering (KCF) tracking algorithm. Deep learning detection is adopted to obtain more accurate spatial position and scale information and reduce the cumulative error. In addition, the improved KCF algorithm is adopted to track and calculate the temporal information correlation of gradient features between video frames, so as to reduce the probability of missing detection and ensure the running speed. In the process of tracking, the spatiotemporal information is fused through feature analysis. A large number of experiment results show that our proposed algorithm has more tracking performance than the traditional KCF algorithm and can efficiently continuously detect and track objects in different complex scenes, which is suitable for engineering application.

scholarly journals An Efficient Real Time Object Tracking Based on HSV Value

2021 ◽  
Vol 9 (11) ◽  
pp. 1197-1203
Author(s):  
Sheikh Summerah
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Color Space ◽  
Video Tracking ◽  
Frame Rate ◽  
Long Distance ◽  
Hsv Color Space ◽  
Video Frames ◽  
Testing Stage

Abstract: This study presents a strategy to automate the process to recognize and track objects using color and motion. Video Tracking is the approach to detect a moving item using a camera across the long distance. The basic goal of video tracking is in successive video frames to link target objects. When objects move quicker in proportion to frame rate, the connection might be particularly difficult. This work develops a method to follow moving objects in real-time utilizing HSV color space values and OpenCV in distinct video frames.. We start by deriving the HSV value of an object to be tracked and then in the testing stage, track the object. It was seen that the objects were tracked with 90% accuracy. Keywords: HSV, OpenCV, Object tracking,

scholarly journals Object Tracking by Detection using YOLO and SORT

2020 ◽  
pp. 224-229
Author(s):  
Heet Thakkar ◽  
Noopur Tambe ◽  
Sanjana Thamke ◽  
Vaishali K. Gaidhane
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
The Past ◽  
Video Frames ◽  
Detection Approach ◽  
Tracking By Detection ◽  
Sort Algorithm ◽  
Tracking Objects

Over the past two decades, computer vision has received a great deal of coverage. Visual object tracking is one of the most important areas of computer vision. Tracking objects is the process of tracking over time a moving object (or several objects). The purpose of visual object tracking in consecutive video frames is to detect or connect target objects. In this paper, we present analysis of tracking-by-detection approach which include detection by YOLO and tracking by SORT algorithm. This paper has information about custom image dataset being trained for 6 specific classes using YOLO and this model is being used in videos for tracking by SORT algorithm. Recognizing a vehicle or pedestrian in an ongoing video is helpful for traffic analysis. The goal of this paper is for analysis and knowledge of the domain.

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