Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images
Ahmad Jalal, Shaharyar Kamal · International Journal of Interactive Multimedia and Artificial Intelligence · 2018
A large number of methods have been designed for efficient BR method and also a lot of comparative studies were evaluated by series of researchers over depth videos [16][17][18] to examine the best algorithms for recognition.These methods mainly interact with depth data using two different approaches: skeleton joints features and depth silhouette features.For example, Oreifej and Liu [19] proposed a new descriptor for behavior recognition using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates.To build the histogram, they created 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal.In [17], Yang et al described an effective method that project depth maps onto three orthogonal planes and accumulate global activities through entire video sequences to generate the Depth Motion Maps (DMM).Histograms of Oriented Gradients (HOG) are then computed to enhance the activity recognition results.In [20], authors proposed a behavior recognition system that deals with motion features as magnitude and directional angular features from body joints information between consecutive frames to recognize daily routine human activities.In [21], authors designed mid-level features from Kinect skeletons by considering the orientations of human body limbs connected by two skeleton joints and each orientation is encoded into different states.They employed frequent pattern mining to pick the most frequent feature values, relevant states of parts in continuous several frames and recognize different activity/actions.However, such methods show better performance and contributions, but different factors having negative impact surrounded each method.Those methods just relied on the skeleton data which became unreliable for postures with self-occlusion.Also, some methods were depended on depth silhouettes information which causes low recognition accuracy especially in case of hidden or missing body parts, fast moving human silhouettes and large distance of subject from the source (i.e.depth camera).Therefore, we elaborate some novel features along with