Iterative Division and Correlograms for Detection and Tracking of Moving Objects

2006 ◽  
pp. 46-55 ◽  
Author(s):  
Rafik Bourezak ◽  
Guillaume-Alexandre Bilodeau
Keyword(s):  
Moving Objects ◽  
Detection And Tracking

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

scholarly journals Object detection and tracking in video sequences: formalization, metrics and results

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 43-60
Author(s):  
R. P. Bohush ◽  
S. V. Ablameyko
Keyword(s):  
Moving Objects ◽  
Human Movement ◽  
Video Sequences ◽  
Multiple Objects ◽  
Movement Trajectories ◽  
Detection And Tracking ◽  
Form Features ◽  
One And Many ◽  
Tracking Objects

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

A hierarchical clustering of features approach for vehicle tracking in traffic environments

2016 ◽  
Vol 9 (4) ◽  
pp. 354-368 ◽  
Author(s):  
Anan Banharnsakun ◽  
Supannee Tanathong
Keyword(s):  
Hierarchical Clustering ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Corner Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Night Time ◽  
Content Type ◽  
Traffic Conditions ◽  
Detection And Tracking

Purpose Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions. The paper aims to discuss these issues. Design/methodology/approach First, each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame. Findings Using the proposed method, it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles. Originality/value This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

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