Retracted: Stationary Object Detection using RetinaNet and Kalman Filter

• Published in: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) pp. 1 - 6
• Main Authors: Kumar, Kunapaneni Sai Ajay, Reddy, Yanamala Maneesha, Babji, Kilaru, Kumar, Chitturi Sai Naveen, Aditya, Man Pawan, Naraharasetty, Dinesh, Kumari, Usha, Rana, Shuvendu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.07.2022
• DOI: 10.1109/ICICCSP53532.2022.9862392

Detection of objects is the most popular research topic nowadays. In this regard Convolution neural network gives a direction to achieve the goal. But detection of the Stationary objects on a live camera become more challenging due to the non-rigid movement of the object. Also, most of the time stationary objects appear to be focal loss in the time of detection. So using CNN for those cases will make the scheme fragile. In this paper, Image segmentation and Kalman filter are used to rectify the focal loss to make the scheme more accurate. Here RetinaNet is used for the implementation of a better object detection scheme. As a result, it is observed that the use of RetinaNet makes the stationary object detection more accurate and the results are acceptable compared to the state of the art model.