954 research outputs found

    Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

    Get PDF
    The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios

    A comprehensive review of vehicle detection using computer vision

    Get PDF
    A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

    Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

    Full text link
    Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity

    Robust Outdoor Vehicle Visual Tracking Based on k-Sparse Stacked Denoising Auto-Encoder

    Get PDF
    Robust visual tracking for outdoor vehicle is still a challenging problem due to large object appearance variations caused by illumination variation, occlusion, and fast motion. In this chapter, k-sparse constraint is added to the encoder part of stacked auto-encoder network to learn more invariant feature of object appearance, and a stacked k-sparse-auto-encoder–based robust outdoor vehicle tracking method under particle filter inference is further proposed to solve the problem of appearance variance during the tracking. Firstly, a stacked denoising auto-encoder is pre-trained to learn the generic feature representation. Then, a k-sparse constraint is added to the stacked denoising auto-encoder, and the encoder of k-sparse stacked denoising auto-encoder is connected with a classification layer to construct a classification neural network. Finally, confidence of each particle is computed by the classification neural network and is used for online tracking under particle filter framework. Comprehensive tracking experiments are conducted on a challenging single-object tracking benchmark. Experimental results show that our tracker outperforms most state-of-the-art trackers
    • …
    corecore