By Md. Atiqur Rahman Ahad
Human motion research and popularity is a comparatively mature box, but one that is frequently now not good understood by means of scholars and researchers. the massive variety of attainable diversifications in human movement and visual appeal, digicam standpoint, and setting, current substantial demanding situations. a few very important and customary difficulties stay unsolved through the pc imaginative and prescient group. even if, many important methods were proposed during the last decade, together with the movement heritage picture (MHI) procedure. this system has acquired major recognition, because it bargains higher robustness and function than different concepts. This paintings provides a finished evaluation of those cutting-edge methods and their functions, with a specific specialize in the MHI technique and its variants.
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Additional info for Motion History Images for Action Recognition and Understanding
Reference  assume that their system observes multiple moving objects via a single, uncalibrated video camera. A trajectory-guided recognition (TGR) approach is proposed as an efficient method for adaptive classification of action. The TGR approach is defined by the Motion History Images that are then recognized via a mixture of Gaussian classifier. Basic steps for : Low-level image processing ⇒ Tracking ⇒ 3D trajectory recovery ⇒ Occlusion reasoning ⇒ Motion recognition • Action line rendering is done by .
Reference  propose the Action Energy Image (AEI) and use eigen decomposition of an AEI in eigen activity space obtained by PCA, which best represents the AEI data in least-square sense. They use Gaussian Mixture Model (GMM) background model for background subtraction. 8 % recognition results in the Weizmann dataset having 9 actions from 9 subjects . Reference  develop a method called Motion Energy Histogram (MEH), which is a histogram-based approach to improve the computation efficiency.
Another 3D-MHI representation, called the Volumetric Motion History Image (VMHI) is proposed by [291, 328]. In another dimension,  presents a novel approach for human action recognition with Histograms of 3D Joint locations (HOJ3D) as a compact representation of postures. They extract the 3D skeletal joint locations from depth maps from Kinect sensor by using Shotton’s method. As the Kinect sensor is an important addition that provides depth images along with color images—in future, we will see more approaches that lead us to better view-invariant methods.