Open AccessVideo Object Detection through Traditional and Deep Learning Methods
Author(s) -
Sita Yadav,
Sandeep Chaware
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d6833.049420
Subject(s) - artificial intelligence , background subtraction , computer science , object detection , video tracking , deep learning , computer vision , object (grammar) , focus (optics) , object class detection , viola–jones object detection framework , feature selection , feature (linguistics) , pattern recognition (psychology) , feature extraction , analytics , machine learning , data mining , pixel , face detection , facial recognition system , linguistics , philosophy , physics , optics
Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper