# A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion
## 1 Introduction
Then, a discriminative model is proposed to encode interactive phrases based on the latent Support Vector Machine (SVM) formulation [16]. The interactive phrases consider more detail in the interaction, so the recognition accuracy is greatly improved, but it does require that all possible rules are predefined. [PERSON] et al. proposed a co-occurrence of a visual words method for human interaction recognition [17]. The method represents the interaction between persons by calculating the number of times that visual words occur simultaneously for each person involved in the interaction. While the implementation of this method is simple, the co-occurrence relationships are not expressive enough to effectively deal with interactions that contain large variations [18; 19]. In general, this kind of method can achieve more accurate and robust results by exploiting rich contexture information in human interactions. However, the recognition results always depend on the accurate feature segmentation of the individual and the stability of the individual behavior model. Based on the above analysis, most of the current interactive behavior recognition methods always address the interaction as a whole to extract features, or separate the interactive behavior into two independent objects to extract features. Obviously, these two kinds of methods cannot reasonably represent the dynamic development process of interactive behavior. A new framework of interactive behavior recognition is therefore proposed by combining those two methods in this paper, as shown in Figure 1. The proposed framework includes the following modules:  
1. **Video segmentation in time domain. ** Interactive behavior is divided into three stages, i.e., start stage, middle stage and end stage in accordance with the distance between two persons. 2. **Region of interest extraction. ** The interaction areas are divided into two independent regions at the start and end stages, when the distance between two persons is extensive. The interaction area is extracted as a whole in the middle stage, when the distance between two persons is smaller and the participants are close to each other or in contact. 3. **Feature extraction. ** The HOG (Histogram of Oriented Gradient) descriptor is used to represent the region of interest (ROI) of each frame. The HOG descriptors are separately extracted in two ROIs at the start and end stages, and the HOG descriptor is extracted in the global ROI in the middle stage. 4. **Interaction model training. ** By using HOG features which have been extracted from each interaction stage. The Hidden Markov model (HMM) is chosen as our action model, because it captures the dynamics of the human action and has robustness in relation to noise. 5. **Interaction recognition. ** When the unknown interaction is taking place, the video segmentation and feature extraction are first performed. Then the probabilities of the test sequence for the sub-HMMs are computed. At the end, the recognition result is obtained by weighted fusing of the likelihood probability of these three stages. Interaction is recognized as a general action method that can fully express global information, and interaction recognition using motion co-occurrence can accurately describe individual behavior, so the proposed method of combining those two kinds of methods is more effective for representing the dynamic process of interactive behavior.