Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

Xi, Zhenghao; Li, Heping; Liu, Huaping; Yang, Bin

doi:10.1155/2014/481719

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…In [4] a graph-based approach has been used too, except that it uses dynamic programming instead of linear programming. These methods are more probable to achieve the global optimum.…”

Section: A Review On Multiple-object Trackingmentioning

confidence: 99%

“…In [9] the HOG features with the bag of video words are used to create the appearance model. The SIFT feature is also a robust feature showing a great performance under the 3 Histogram of Oriented Gradient 4 Simple Online and Real time Tracking with a Deep association metric change of transformation, illumination and even occlusion. In [10] only the SIFT features are used to make the appearance model.…”

Section: A Review On Multiple-object Trackingmentioning

confidence: 99%

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Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

Bafghi

Shoushtarian

2020

2020 International Conference on Machine Vision and Image Processing (MVIP)

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In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features.

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“…In [4] a graph-based approach has been used too, except that it uses dynamic programming instead of linear programming. These methods are more probable to achieve the global optimum.…”

Section: A Review On Multiple-object Trackingmentioning

confidence: 99%

Section: A Review On Multiple-object Trackingmentioning

confidence: 99%

Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

Bafghi

Shoushtarian

2020

2020 International Conference on Machine Vision and Image Processing (MVIP)

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“…Many recent works process sequences in batch mode, using a graph-based representation with detections as nodes and possible assignments as edges. The optimization is then cast as a linear program solved to (near) global optimality with relaxation, min-cost or shortest path algorithms [14]- [16]. More complex optimization schemes include MCMC [17] and discrete-continuous settings [18].…”

Section: A Data Association In Motmentioning

confidence: 99%

FANTrack: 3D Multi-Object Tracking with Feature Association Network

Baser

Balasubramanian

Bhattacharyya

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at https://git.uwaterloo.ca/wise-lab/fantrack.

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“…To calculate numerous safe paths, the k -shortest path search algorithm was applied. Other applications of the k -shortest path search algorithm include adjusting traffic flows from overloaded links to underutilized links in a telecommunication network [38] and detecting objects in individual frames of a video [39, 40]. Likewise, there have been numerous researches employing the shortest path search algorithm in the bioinformatics area.…”

Section: Introductionmentioning

confidence: 99%

Inference on chains of disease progression based on disease networks

2019

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Motivation Disease progression originates from the concept that an individual disease may go through different changes as it evolves, and such changes can cause new diseases. It is important to find a progression between diseases since knowing the prior-posterior relationship beforehand can prevent further complications or evolutions to other diseases. Furthermore, the series of progressions can be represented in the form of a chain, which enables us to readily infer successive influences from one disease to another after many passages through other diseases. Methods In this paper, we propose a systematic approach for finding a disease progression chain from a source disease to a target one via exploring a disease network. The network is constructed based on various sets of biomedical data. To find the most influential progression chains, the k -shortest path search algorithm is employed. The most representative algorithms such as A*, Dijkstra, and Yen’s are incorporated into the proposed method. Results A disease network consisting of 3,302 diseases was constructed based on four sources of biomedical data: disease-protein relations, biological pathways, clinical history, and biomedical literature information. The last three sets of data contain prior-posterior information, and they endow directionality on the edges of the network. The results were interesting and informative: for example, when colitis and respiratory insufficiency were set as a source disease and a target one, respectively, five progression chains were found within several seconds (when k = 5). Each chain was provided with a progression score, which indicates the strength of plausibility relative to others. Similarly, the proposed method can be expanded to any pair of source-target diseases in the network. This can be utilized as a preliminary tool for inferring complications or progressions between diseases.

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Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

Cited by 7 publications

References 20 publications

Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

FANTrack: 3D Multi-Object Tracking with Feature Association Network

Inference on chains of disease progression based on disease networks

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