Object Detection Mechanism using Deep CNN Model

Object detection and tracking are important roles in computer vision algorithms. Detecting and tracking objects have many challenges like detecting objects efficiently in both indoor and outdoor circumstances and avoiding occlusion. Many traditional methods are ineffective in these circumstances. Recently, deep learning methods are preferred over traditional machine learning models due to its increased efficiency and accuracy. Object detection is a key topic in image processing and is used for detecting identifying and locating objects by drawing the bounding boxes to those objects. Object detection has various applications in various fields like security medical field and defense. This article discusses about the implementation of object detection for video and digital images and the detection of objects by web camera for higher accuracy and real-time implementation. Using a Convolutional Neural Network (CNN) result in the disadvantage of generating many feature maps for preventing occlusion increasing the future maps doesn’t increase the performance. So, an advanced and better neural network model is required for solving those problems. This research work has implemented object detection to analyzed digital image/video by using a web camera and deep learning method to achieve better performance with a comparatively high accuracy than existing methods.