Efficient Vehicle Detection and Classification for Traffic Surveillance System

Ukani, Vijay; Garg, Sanjay; Patel, Chirag; Tank, Hetali

doi:10.1007/978-981-10-5427-3_51

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…They detected vehicles with a YOLOv3 model and classified them into various categories with an SVM. Similar to this, Deka and Singh [5] suggested a deep learning-based system for classifying and detecting vehicles that utilised a multilayer perceptron (MLP) classifier and a Faster R-CNN model for detection. They obtained high precision in identifying and classifying vehicles in traffic surveillance situations.…”

Section: Introductionmentioning

confidence: 90%

Real time two-wheeler recognition for Surveillance

Ganesh,

Perumaal,

Preethi

et al. 2023

J. Phys.: Conf. Ser.

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In today’s intelligent transportation systems, vision-based traffic tracking systems and automatic vehicle make and model recognition is of significant importance for surveillance. From the literature, vehicle make and model recognition has been implemented for four wheelers easily due to the presence of logo in the frontal and rear parts. But it is not feasible for two wheelers due to the insignificant differential features. Hence in this work, a robust two-wheeler recognition system has been developed with frontal, rear and side images of two wheelers which is implementable in edge devices. The contributions of the paper are (i) Development of a two-wheeler dataset with the frontal, rear and side images of two classes in Indian Scenario. (ii) Development of a two-wheeler recognition framework implementable in Raspberry pi-based devices. (iii) Evaluation of the framework in real time outdoor environment. From the experimental results it is observed that the proposed network classifies two wheelers in real time at 4s with 94% accuracy.

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Section: Introductionmentioning

confidence: 90%

Real time two-wheeler recognition for Surveillance

Ganesh,

Perumaal,

Preethi

et al. 2023

J. Phys.: Conf. Ser.

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“…Ukani et al [16] introduced a vehicle detection and classification system that considers video to analyze traffic. They extracted SIFT features for further processing by incorporating the artificial neural network as a classifier as well as a support vector machine (SVM).…”

Section: A Machine Learning-based Vehicle Detectionmentioning

confidence: 99%

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

et al. 2023

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Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.

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“…Popular background subtraction techniques include frame differencing [ 23 , 24 ], Shadow removal [ 25 ], Gaussian mixture model(GMM) [ 26 ], and CNN-based background removal [ 27 ]. However, an algorithm for moving object detection without any background modeling was presented in [ 28 , 29 , 30 ], and the detailed procedure is given below.…”

Section: Proposed Frameworkmentioning

confidence: 99%

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Patel

Labana

Pandya

et al. 2020

Sensors

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Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.

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Efficient Vehicle Detection and Classification for Traffic Surveillance System

Cited by 14 publications

References 11 publications

Real time two-wheeler recognition for Surveillance

Real time two-wheeler recognition for Surveillance

Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

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