Spatio-Temporal Relationship Match: Video Structure Comparison for Recognition of Complex Human Activities M. S. Ryoo and J. K. Aggarwal ICCV2009 Slide 2 Introduction Human activity recognition, an automated detection of ongoing activities from video is an important problem. This technology can use on surveillance systems, robots, human-computer interface. When using on serveillance systems,automaically detect violent activities is very important. Slide 3 Introduction Spatial-temporal feature-based approaches have been proposed by many researchers. The method above have benn successful on short video containing simple action such as walking and waving. In real-world applications, actions and activities are seldom like this. Slide 4 Related works Methods focused on tracking persons and bodies are developed [4,11],but their results rely on background subtraction. Approaches that analyze a 3-S XYT volume gained particular in past few years[3,5,6,9,13,16], they extracted relationship on features and trained a model. Slide 5  P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behaviorrecognition via sparse spatio- temporal features. In IEEEInternational Workshop on VS-PETS, pages 6572, 2005.  S. Hongeng, R. Nevatia, and F. Bremond. Video-based eventrecognition: activity representation and probabilistic recognitionmethods. CVIU, 96(2):129162, 2004.  H. Jhuang, T. Serre, L. Wolf, and T. Poggio. A biologicallyinspired system for action recognition. In ICCV, 2007.  I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld.Learning realistic human actions from movies. In CVPR,2008.  J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. IJCV, 79(3), Sep 2008.  M. S. Ryoo and J. K. Aggarwal. Semantic representation and recognition of continued and recursive human activities. IJCV, 82(1):124, April 2009.  C. Schuldt, I. Laptev, and B. Caputo. Recognizing humanactions: a local svm approach. In ICPR, 2004.  S.-F. Wong, T.-K. Kim, and R. Cipolla. Learning motion categories using both semantic and structural information. In CVPR, 2007. Slide 6 Related works In this paper, we propose a new spatial- temporal feature-based methodology. Kernel functions are built on relationship between features. After training features, match function uses for matching test data. Slide 7 Example matching result Slide 8 Spatio-temporal relationship match The method is based on matching two videos and output a real number for result. K : V x V R V -> input video, R-> result Slide 9 Features and their relations A spatial-temporal feature extractor [3,14]detects each interest point locating a salient change. Slide 10 Slide 11 Features and their relations f= (f des,f loc ) f des ->descriptor,f loc -> 3-D coordinate The features are clustered into k types using k-means on f des. Slide 12 Features and their relations Each f loc have n elements, f 1 loc,..f n loc. There are types to describe temporal relations: Slide 13 Features and their relations Spatial relation are described below: Slide 14 Slide 15 Features and their relations Slide 16 Human activity recognition Our system maintains one training dataset D per activity . Let D m extracted from mth training video in the set D , then use the matching function. Slide 17 Localization Slide 18 Hierarchical recognition We can combine low-level action into high- level action. For instance, hand-shake includes two sub- action, arm streching and arm withdrawing. Detecting hand-shake may like : st1 before wd1,st2 before wd2, st1 equals st2,wd1 equals wd2. Slide 19 Experiments The dataset is UT-interaction dataset. The actions are performed by actors, each video contains shake hands,point,hug,push,kick and punch. Slide 20 Experiments Slide 21 Slide 22 Conclusion This method rely on the extracted feature and spatial-temporal relationship on features. Can hierarchically detect high-level actions. Miss-detect on unusual feature combination.
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