A visual cognizance based multi-resolution descriptor for human action recognition using key pose

Abstract

Human activity recognition using videos sequences is a well-known phenomenon which has many real-life applications such as daily assistive living, security and surveillance, patient monitoring, robotics, and sports analysis. Recently, single or still images based action recognition is becoming very popular due to spatial cues present in an image and required less computation. Hence, a robust framework is constructed by computation of textural and spatial cues of still images at multi-resolution. A fuzzy inference model is used to select the single key image from action video sequences using maximum histogram distance between stacks of frames. To represent, these key pose images the textural traits at various orientations and scales are extracted using Gabor wavelet while shape traits are computed through a multilevel approach called Spatial Edge Distribution of Gradients (SEDGs). Finally, a hybrid model of action descriptor is developed using shape and textural evidence, which is known as Extended Multi-Resolution Features (EMRFs) model. The highest classification accuracy is achieved through SVM classifier on various human action datasets: Weizmann Action (100%), KTH (95.35%), Ballet (92.75%), and UCF YouTube (96.36%). The highest accuracy achieved on these datasets are compared with similar state-of-the-art approaches and EMRFs shows superior performance.