Human Behavior Recognition based on Conditional Random Field and Bag-Of-Visual-Words Semantic Model

Abstract

Although current gymnastics action detection algorithm has good detection and recognition results but cannot effectively identify a variety of consecutive gymnastic actions and many gymnastics has high false rate. So on this paper we improve the CRF model and bag-of-visual-words semantic model, combine the advantages of both models to build a hierarchical model for behavior recognition, first we create a hierarchical semantic mark CRFs model, the model is divided into upper and lower layers and a gymnastic image filter that based on bag-of-visual-words semantic model. Identifying the error action image by the semantic, not only in line with the cognitive process of machine vision, and can effectively compensate the existing algorithm correcting the high false positives rate. Experiments show that by combining the two algorithms we can detect errors gymnastics image effectively, recognition rate compared with other algorithms improved, and the false detection rate reduced.

Using Shape Descriptors for UAV Detection

“…BoW approach is known as an effective computer vision algorithm for general object classification problem, with applications such as [16], [17], [18]. Rather than CNNs, [15] uses SURF based keypoint features of grayscale drone, bird and background image patches.…”

An optimized classification method for human behavioral patterns recognition

“…They considered a home monitoring scenario for which the system was learned to recognize a set of complex ADL-related behaviors. A behavioral recognition system with a Conditional Random Field model is defined in [10]. The system is based on a hierarchical semantic model exploiting the dynamic and hidden features of behavior and applying a high-level semantic tree.…”