Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.


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    Title :

    Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques


    Contributors:

    Publication date :

    2021


    Remarks:

    Embedded Selforganising Systems, 8 (2), p. 8-14



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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