Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

Kachach, Redouane; Plaza, José María

doi:10.1117/1.jei.25.3.033021

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…MOG is a technique that first applied to the problem of background subtraction. TrafficMonitor [ 13 ] makes use of an improved version of the proposed MOG by Zivkovic [ 30 ]. The advantage of this method is that for each pixel, the number of Gaussian to be used can be adapted.…”

Section: Related Workmentioning

confidence: 99%

“…When we search for a vehicle in ( t ), we should find it in a small circular radius around the position of that same vehicle in ( t − 1). Spatial proximity tracking in TrafficSensor is based on [ 13 ]. It estimates the area where you should locate a vehicle based on its position in ( t − 1).…”

Section: Trafficsensor: a Deep Learning-based Traffic Monitoring Toolmentioning

confidence: 99%

“…The study presents a vision-based traffic monitoring system, named TrafficSensor, that includes a robust vehicle detection and classification algorithm and a new technique for dealing with occlusions [ 10 – 12 ]. It is the evolution of a previous system [ 13 ] towards a higher reliability and good performance even in challenging lightning or weather conditions, and poor camera resolution while keeping real-time operation. TrafficSensor is based on the use of a fixed camera to detect and monitor vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Robust Real‐Time Traffic Surveillance with Deep Learning

Fernandez

Plaza

Fernández

et al. 2021

Computational Intelligence and Neuroscience

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Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for object detection and classification, especially in the last years, made this area even more appealing due to the effectiveness of these methods. This study presents TrafficSensor, a system that employs deep learning techniques for automatic vehicle tracking and classification on highways using a calibrated and fixed camera. A new traffic image dataset was created to train the models, which includes real traffic images in poor lightning or weather conditions and low-resolution images. The proposed system consists mainly of two modules, first one responsible of vehicle detection and classification and a second one for vehicle tracking. For the first module, several neural models were tested and objectively compared, and finally, the YOLOv3 and YOLOv4-based network trained on the new traffic dataset were selected. The second module combines a simple spatial association algorithm with a more sophisticated KLT (Kanade–Lucas–Tomasi) tracker to follow the vehicles on the road. Several experiments have been conducted on challenging traffic videos in order to validate the system with real data. Experimental results show that the proposed system is able to successfully detect, track, and classify vehicles traveling on a highway on real time.

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Section: Related Workmentioning

confidence: 99%

Section: Trafficsensor: a Deep Learning-based Traffic Monitoring Toolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Real‐Time Traffic Surveillance with Deep Learning

Fernandez

Plaza

Fernández

et al. 2021

Computational Intelligence and Neuroscience

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“…A vehicle tracking in this context is the basis for higher-level tasks such as traffic control, event detection, extraction of information from the traffic flow, etc. [2][3][4]. Typical problems for reliable vehicle tracking in these circumstances are variable background and shadows, variable existence of other close objects (moving or stationary), partial or even full occlusions by elements of the scene, variable size of an object during the sequence, etc.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

Adv. Electr. Comp. Eng.

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This work is oriented toward video tracking of vehicles in a typical traffic environment, based on particle filters. The proposed tracking algorithm is based on simultaneous usage of three different image features -color, edge orientation, and texture. All three features are related to the contents of a rectangular window that includes both the vehicle that is tracked and local background and they are represented in the form of appropriate histograms. Based on individual estimates produced by every single feature, the resultant estimate is made by their weighted averaged. Weighting factors are adaptively changing depending on the quality of a particular feature, estimated by calculations of average similarities between the reference window and the set of windows on particles' positions. The tracking accuracies of single-feature and three-features-based filters have been verified using the set of traffic sequences illustrating the presence of typical disturbances (shadows, partial and full occlusions, maneuvering etc.).

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“…As for the classifiers for target recognition, the widely used ones include support vector machine (SVM) [9], Adaboost [10], K-nearest neighbor (KNN) [11], sparse representation (SR) [12], etc. These classifiers can only work well under two conditions: (1) the training and testing samples are drawn from the same feature space and distribution; (2) there exist sufficient training samples to train an effective classifier.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

2020

KSII TIIS

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Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

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Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

Cited by 13 publications

References 34 publications

Robust Real‐Time Traffic Surveillance with Deep Learning

Robust Real‐Time Traffic Surveillance with Deep Learning

Untitled

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

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