scholarly journals Estimation for Motion in Tracking and Detection Objects with Kalman Filter

2020 ◽  
Author(s):  
Afef Salhi ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Visual Motion ◽  
Optimal Solution ◽  
Block Matching ◽  
Detection And Tracking ◽  
Tracking Object ◽  
Tracking Objects ◽  
Localization And Tracking ◽  
Prediction Parameters

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

scholarly journals Tracking Objects on Detector Response using Extended Kalman Filter

2020 ◽  
Vol 9 (4) ◽  
pp. 1614-1620
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Video Frame ◽  
Detector Response ◽  
Data Set ◽  
Detection And Tracking ◽  
Cumulative Summation ◽  
Tracking Objects

Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.

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.

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 MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Muhamad Soleh ◽  
Grafika Jati ◽  
Muhammad Hafizhuddin Hilman
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Optical Flow ◽  
Motion Blur ◽  
Research Topic ◽  
Vehicle Tracking ◽  
Flow Density ◽  
Illumination Change ◽  
Dynamic Background ◽  
Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

Visual Attention Guided Object Detection and Tracking

2016 ◽  
pp. 99-114
Author(s):  
Debi Prosad Dogra
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Attention ◽  
Visual Saliency ◽  
Video Object ◽  
Salient Region Detection ◽  
Region Detection ◽  
Video Object Tracking ◽  
Detection And Tracking ◽  
Pros And Cons

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments

2017 ◽  
Vol 7 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Enrique Domínguez
Keyword(s):  
Video Surveillance ◽  
Extraction Process ◽  
Traffic Monitoring ◽  
Point Of View ◽  
Growing Neural Gas ◽  
Feature Extraction Method ◽  
Vehicle Type ◽  
Neural Gas ◽  
Detection And Tracking ◽  
Tracking Object

Automated video surveillance presents a great amount of applications and one of them is traffic monitoring. Vehicle type detection can provide information about the characteristics of the traffic flow to human traffic controllers in order to facilitate their decision-making process. A video surveillance system is proposed in this work to execute such classification. First of all, a foreground detection and tracking object process has been carried out. Once the vehicles are detected, a feature extraction method obtains the most significant features of this detected vehicles. When the extraction process is done, the vehicle types are determined by employing a set of Growing Neural Gas neural networks. The performance of the proposal has been analyzed from a qualitative and quantitative point of view by using a set of benchmark traffic video sequences, with acceptable results.

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