A DEEP LEARNING APPROACH FOR THE ANALYSIS AND DETECTION OF OBJECT IN VIDEO FRAMES USING YOLO FASTER RCNN
Object detection often refers to a collection of generic computer vision tasks which potentially identifies objects from the given video inputs. As object detection combines two main tasks like image classification and object localisation which eventually identifies one or more objects in a specified image frame. The space in which this research is very popular is one where researchers continue developing new aspects in detecting objects, and in various areas including autonomous driving, health-care monitoring, anomaly detection etc. Traditional object detection is done using shallow features and handcrafted architecture which eventually doesn’t give effective results. So, to overcome this, the use of advanced technology such as Deep learning comes into play as it has a wide hand in this field. Thereby this paper brings an effective object detection model from video frames in which initially a)Data Collection from ImageNet VID and CIFAR-10 video analysis b) Feature extraction using a convolutional autoencoder c) feature selection using SE-block d) Classification using integration of Yolo-Faster-RCNN. The study shows that the proposed method outperforms with 95% accuracy when compared with other state-of-art models.
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