Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation

Guerrero-Gómez-Olmedo, Ricardo; López-Sastre, Roberto J.; Maldonado-Bascón, Saturnino; Fernández-Caballero, Antonio

doi:10.1007/978-3-642-38622-0_32

Cited by 74 publications

(41 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this work, the performance of the proposed system was tested on real traffic videos: three videos, V1, V2, and V3, recorded in Guadalajara, Mexico; two videos (V4, V5) obtained from the GRAM Road-Traffic Monitoring (GRAM-RTM) dataset [56,57] (the video named V4 corresponds to video M-30, and the video named V5 is video M-30-HD); and video (V6, V7, and V8) recorded in Britain’s M6 motorway (see [58]). …”

Section: Resultsmentioning

confidence: 99%

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Velazquez-Pupo

Sierra-Romero

Torres-Roman

et al. 2018

Sensors

View full text Add to dashboard Cite

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

show abstract

Section: Resultsmentioning

confidence: 99%

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Velazquez-Pupo

Sierra-Romero

Torres-Roman

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…This warning showed that the traffic on that image is heavy [22]. The GRAM Road Traffic Monitoring is used for the vehicle detection and counting, and it is adopted by many recent researches for the evaluation of the proposed techniques [23]. In 2013 another implementation is done that used Histogram of oriented gradients as well as the Haar features for the detection of the vehicles.…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

et al. 2020

View full text Add to dashboard Cite

In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.

show abstract

“…To overcome these above-mentioned problems, an improved V-ITS system was presented through advanced deep learning algorithms to get robust results. In the past studies, the deep learning algorithms [24][25][26][27][28][29][30][31][32][33][34][35][36] [38] model is trained in different roads samples along with diverse environmental conditions. Those samples were obtained from GRAM Road-Traffic Monitoring (GRAM-RTM) dataset [37].…”

Section: Related Workmentioning

confidence: 99%

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Abbas¹

2019

ijacsa

View full text Add to dashboard Cite

Vehicle monitoring is a challenging task for videobased intelligent transportation system (V-ITS). Nowadays, the V-ITS system has a significant socioeconomic impact on the development of smart cities and always demand to monitor different traffic parameters. It noticed that traffic accidents are exceeded throughout the world with the percentage of 1.7%. The increase in accidents and the percentage of deaths are due to the people that don't abide by the traffic rules. To address these challenges, an improved V-ITS system is developed in this paper to detect and track vehicles and driver's activities during highway driving. This improved V-ITS system is capable to do automatic traffic management that saves traffic accidents. It provides the feature of a real-time detection algorithm for driver immediate line overrun, speed limit overrun and yellow-line driving. To develop this V-ITS system, a pre-trained convolutional neural network (CNN) model with 4-layer architecture was developed and then deep-belief network (DBN) model was utilized to recognize illegal activities. To implement V-ITS system, OpenCV and python tools are mainly utilized. The GRAM-RTM online free data sets were used to test the performance of V-ITS system. The overall significance of this intelligent V-ITS system is comparable to other state-of-the-art systems. The real-time experimental results indicate that the V-ITS system can be used to reduce the number of accidents and ensure the safety of passengers as well as pedestrians.

show abstract

Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation

Cited by 74 publications

References 19 publications

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Contact Info

Product

Resources

About