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 observations. In the fifth research article referred, an attempt has been made to recognize the Human Rights Violation activities using the Deep Convolutional Neural Networks. In the sixth research article, Convolutional LSTM is used for the purpose of detecting violent videos. The seventh paper introduces a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation. In the eighth research paper, a new temporal transition layer (TTL) that models variable temporal convolution kernel depths is embedded into 3D CNN to form T3D (Temporal 3D Convnets). Transferring knowledge from a pre-trained 2D CNN to a 3D CNN reduces the number of training samples required for 3D CNNs.

CNN SVM MiCT Glimpse Clouds Two-stream Bayesian Encoder-Decoder Pose estimation Heat Maps ConvLSTM Two-stream 3D CNN TTL T3D.