Portal de Periódicos da CAPES

Assessment of Object Detection Using Deep Convolutional Neural Networks

2018; Springer Nature; Linguagem: Inglês

10.1007/978-981-10-7245-1_45

2194-5357

Ajeet Ram Pathak, Manjusha Pandey, Siddharth Swarup Rautaray, Karishma Pawar,

Video Surveillance and Tracking Methods

Detecting the objects from images and videos has always been the point of active research area for the applications of computer vision and artificial intelligence namely robotics, self-driving cars, automated video surveillance, crowd management, home automation and manufacturing industries, activity recognition systems, medical imaging, and biometrics. The recent years witnessed the boom of deep learning technology for its effective performance on image classification and detection challenges in visual recognition competitions like PASCAL VOC, Microsoft COCO, and ImageNet. Deep convolutional neural networks have provided promising results for object detection by alleviating the need for human expertise for manually handcrafting the features for extraction. It allows the model to learn automatically by letting the neural network to be trained on large-scale image data using powerful and robust GPUs in a parallel way, thus, reducing training time. This paper aims to highlight the state-of-the-art approaches based on the deep convolutional neural networks especially designed for object detection from images.