運用人體輪廓特徵與靜態動作分類為基礎的行為分析系統

隨著科技進步，監控系統已經被廣泛的運用在許多環境。有別於過去單純錄影功能的監控系統，目前研究方向已漸漸邁向智慧型監控。智慧型監控主要是利用電腦視覺的技術，讓監控系統可以自動擷取到人的位置，進而分析其在視訊中的各種行為。本論文的研究目的，即在於利用監控攝影機所拍下的視訊中，偵測其中人類物件的位置，進行追蹤與分析，並將人類物件所表現出的動作分割成多個靜態動作存入資料庫後進行比對，以辨識該種行為屬於何種行為。本系統設計包含擷取視訊物件的前置處理、物件的正規化、人類特徵擷取、建立行為資料庫與人類行為分析等步驟。本系統以在20個標準行為所建立出來的行為資料庫為訓練樣本，再經由32個未知行為樣本測試，可以成功的分辨出行走和跑步這兩種常見的人類行為，其中行走與跑步所包含2322及610之靜態動作其判斷率均達到90%以上。總而言之，人類的行為是由多個動作所組成的，利用這種方法未來將可以經由各種靜態動作的組合分析出多種的人類行為，將此方法應用在監控系統上將可以應付各種不同行為分析的需求。

關鍵字

視訊處理；行為分析；監控系統

並列摘要

With the advance of technology, the surveillance system has been widely used in many environments. Computer vision and multimedia techniques make feasible the development of new and “smart” surveillance systems that are different from traditional systems with only the recording function. In recent research, the surveillance system has been designed to automatically detect the location of human in video and to analyze the human behavior. In this paper, the objective is to identify and track the location of human, to match various static posture states in human behavior with respect to an established database, and to recognize the actual human behavior. The system design includes pre-processing of objects, object normalization, feature extraction of human, and collection of a database of human behavior for further analysis. The system was developed using 20 standard behavior as training samples, and was then evaluated with 32 unknown behavior samples. In addition, 2322 and 610 static poses were also evaluated. Our preliminary results demonstrated a 90% of classification in recognizing the two human behavior, namely the “walking” or “running”. In conclusion, human behavior is composed of many static posture states. Our methods could be used to analyze the combination of these states, and ultimately applied in surveillance systems with the need to recognize various humanbehavior.

並列關鍵字

video processing ； behavior analysis ； surveillance system

參考文獻

[1] K. Hara, T. Omori and R. Ueno, “Detection of Unusual Human Behavior in Intelligent House,” IEEE Proceedings on Neural Networks for Signal

Processing,pp.697-706, 2002.

[2] L. Panini, R. Cucchiara, “A Machine Learning Approach for Human Posture Detection in Domotics Applications,” IEEE Proceedings on

[3] T. Harada, T. Mori, T. Sato, “Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures,” IEEE International Conference on Systems, Man and Cybernetics, vol. 2, no.10-13,pp.1595-1600, 2004.

[5] I. Haritaoglu, D. Harwood, and L. S. Davis, “W4：Real-Time Surveillance of People and Their Activities,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22,No. 8, pp. 809-830, August 2000.

國際替代計量

運用人體輪廓特徵與靜態動作分類為基礎的行為分析系統

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