Proceedings ArticleDOI
Vehicle detection, tracking and classification in urban traffic
Zezhi Chen,Tim Ellis,Sergio A. Velastin +2 more
- pp 951-956
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TLDR
This paper presents a system for vehicle detection, tracking and classification from roadside CCTV, using a combination of a vehicle silhouette and intensity-based pyramid HOG features extracted following background subtraction, classifying foreground blobs with majority voting.Abstract:
This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Extensive experiments with real world data have been undertaken to evaluate system performance. The best performance results from training a SVM (Support Vector Machine) using a combination of a vehicle silhouette and intensity-based pyramid HOG features extracted following background subtraction, classifying foreground blobs with majority voting. The evaluation results from the videos are encouraging: for a detection rate of 96.39%, the false positive rate is only 1.36% and false negative rate 4.97%. Even including challenging weather conditions, classification accuracy is 94.69%.read more
Citations
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Journal ArticleDOI
SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection
TL;DR: A scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales is presented and a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects is presented.
Proceedings ArticleDOI
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
TL;DR: Zhang et al. as discussed by the authors designed a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with LSTM in a residual learning fashion.
Journal ArticleDOI
Hierarchical and Networked Vehicle Surveillance in ITS: A Survey
TL;DR: This work analyzes the existing challenges in video-based surveillance systems for the vehicle and presents a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques.
Posted Content
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
TL;DR: Zhang et al. as mentioned in this paper designed a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with LSTM in a residual learning fashion.
Journal ArticleDOI
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
TL;DR: This article presents a review of state-of-the-art traffic monitoring systems focusing on the major functionality–vehicle classification and discusses hardware/software design, deployment experience, and system performance of vehicle classification systems.
References
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Chris Stauffer,W.E.L. Grimson +1 more
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.
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Chris Stauffer,W.E.L. Grimson +1 more
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BookDOI
An Introduction to the Kalman Filter
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TL;DR: The discrete Kalman filter as mentioned in this paper is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error.
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