A Review on Action Recognition and Action Prediction of Human(s) using Deep Learning Approaches

Human Action Recognition and Prediction are some of the hot topics in Computer Vision these days. It has its formidable contribution in the Anomaly detection. Many research scientists have been working in this field. Many new algorithms have been tried out in recent decades. In this paper, eight such approaches proposed in eight research papers have been reviewed. Compared to their counterparts for still images (the 2D CNNs for visual recognition), the 3D CNNs are considered to be comparatively less efficient, due to the limitations like high training complexity of spatio-temporal fusion and huge memory cost. So in the first referred paper the authors have proposed MiCT (Mixed Convolution Tube – for videos) with the right use of both 2D CNNs and 3D CNNs which reduces the training time. In the second research paper, the glimpse sequences in each frame correspond to interest points in the scene that are relevant to the classified activities. Unlike the last referred paper, the third referred paper presents a novel method to recognize human action as the evolution of pose estimation maps. The fourth referred paper presents a model for long term prediction of pedestrians from on-board