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Proceedings Article•DOI•

Efficient detection tracking of multiple moving objects in temporal domain

08 Apr 2016-pp 1-6
TL;DR: The proposed Decomposition with Temporal Domain (DTD) strategy can identify and track objects all the while and reduces the False Alarms Rate (FAR) and increases True Positive Rate (TPR).
Abstract: In video surveillance the moving object detection and tracking are important research area of computer vision. Tracking moving objects in a real time environment is not easy because of continual deformation of objects during movement. The moving item has many attributes in both temporal and spatial spaces. In the spatial area, objects vary in size while in temporal area they vary in moving speed. To track multiple object in video with optimal window size the Decomposition with Temporal Domain (DTD) is proposed. Thus the moving objects are detected and tracked until it disappears or losses it motion. The proposed strategy can identify and track objects all the while. It reduces the False Alarms Rate (FAR) and increases True Positive Rate (TPR). Moving items with various speeds and sizes are tracked with their optimal window size and the possible shadow is eliminated by the multi frame difference method. The proposed method is compared with Kalman filter and Optical Flow (OF) of Lucas-Kanade method using probabilistic approaches such as FAR and TPR performance is analyzed. Different test results have affirmed the legitimacy and adequacy of the proposed technique.
Citations
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Journal Article•DOI•
TL;DR: A method of multiple moving object detection and tracking by combining background subtraction and K-means clustering is proposed and it is capable of handling merging and splitting of moving objects using spatial information.
Abstract: Object detection and tracking is a fundamental, challenging task in computer vision because of the difficulties in tracking. Continuous deformation of objects during movement and background clutter leads to poor tracking. In this paper, a method of multiple moving object detection and tracking by combining background subtraction and K-means clustering is proposed. The proposed method can handle objects occlusion, shadows and camera jitter. Background subtraction filters irrelevant information, and K-means clustering is employed to select the moving object from the remaining information, and it is capable of handling merging and splitting of moving objects using spatial information. Experimental results show that the proposed method is robust when compared to other techniques.

24 citations

Journal Article•DOI•
TL;DR: The qualitative and quantitative comparison results show that the proposed algorithm not only has good object segmentation performance but also improves the tracking performance of completely and partially occluded objects, which is superior to the tested state-of-the-art tracking approaches.
Abstract: Tracking multiple people in crowds is a fundamental and essential task in the multimedia field. It is often hindered by difficulties, such as dynamic occlusion between objects, cluttered background, and abrupt illumination changes. To respond to this need, in this paper, we combine deep and depth to build a stereo tracking system for crowds. The core of the system is the fusion of the advantages of deep learning and depth information, which is exploited to achieve object segmentation and improve the multiobject tracking performance in severe occlusion. More specifically, first, to obtain more accurate detection observations in the tracking system, we present a novel object-level segmentation method. This method combines the effective detection results of deep learning with depth information to obtain precise object segmentation results. Then, we integrate the segmentation results and three-dimensional (3-D) information to extract 2-D and 3-D characteristics to represent the target, and design three similarity models to realize a stereo tracking method through data association in crowds. Finally, we build a diverse stereo dataset including various challenging indoor and outdoor scenes. The comprehensive experiments verify the effective and robust tracking performance of our system in various scenarios, and the system has rich output results including segmentation results, target distance, and tracking results. Moreover, the qualitative and quantitative comparison results show that the proposed algorithm not only has good object segmentation performance but also improves the tracking performance of completely and partially occluded objects, which is superior to the tested state-of-the-art tracking approaches.

13 citations


Cites background from "Efficient detection tracking of mul..."

  • ...MULTIOBJECT tracking plays a vital role in the multimedia field [1], [2]....

    [...]

Patent•
20 Jul 2018
TL;DR: In this paper, a training method and device of a neural network model for image processing is presented, where video frames adjacent in time are obtained, the neural network is adjusted according to the time loss and the characteristic loss, and the step of obtaining the video frames in time is returned to, and training is not stopped until the neural model satisfies a training ending condition.
Abstract: The invention relates to a training method and device of a neural network model for image processing. The method comprises that video frames adjacent in time are obtained; the neural network model output intermediate images corresponding to the video frames respectively; change of the video frames in relatively earlier time and optical flow information of the video frames in relatively later timeare obtained; and image formed by changing the intermediate image, corresponding to the video frame in relatively earlier time, according to the optical flow information is obtained; time loss betweenthe intermediate image corresponding to the video frame in relatively later time and an image after change is obtained; characteristic loss between the intermediate image and a target characteristicimage is obtained; and the neural network model is adjusted according to the time loss and the characteristic loss, the step of obtaining the video frames adjacent in time is returned to, and trainingis not stopped until the neural network model satisfies a training ending condition. Thus, the neural network model obtained by training has a better video characteristic conversion effect.

6 citations

Journal Article•DOI•
01 Nov 2017
TL;DR: A traffic intensity monitoring system based on the Macroscopic Urban Traffic model is proposed using computer vision as its source and another program running a neural network classification which can identify and differentiate the vehicle type is implanted.
Abstract: Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

4 citations

References
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Proceedings Article•DOI•
23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Abstract: A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

7,660 citations

Journal Article•DOI•
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Abstract: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

5,318 citations


"Efficient detection tracking of mul..." refers background in this paper

  • ...Object tracking [2] is an important task within the eld of computer vision....

    [...]

Journal Article•DOI•
TL;DR: Zhang et al. as mentioned in this paper proposed a unified framework named detecting contiguous outliers in the LOw-rank representation (DECOLOR), which integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm.
Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.

579 citations

Posted Content•
TL;DR: This paper presents a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR), which integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently.
Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.

509 citations

Journal Article•DOI•
TL;DR: This work aims to initiate a rigorous and comprehensive review of RPCA-PCP based methods for testing and ranking existing algorithms for foreground detection and investigates how these methods are solved and if incremental algorithms and real-time implementations can be achieved.

453 citations