Weilun Lao, Jungong Han, and Peter H.N. de With, Fellow, IEEE IEEE Transactions on Consumer Electronics, Vol. 55, No. 2, MAY 2009 Automatic Video-Based Human Motion Analyzer for Consumer Surveillance System

Outline Introduction Literature on surveillance video analysis Requirements of surveillance analysis systems Overview of proposed visual motion analysis system Techniques for human motion analysis Experimental results

Introduction Video surveillance can contribute to the safety of people in the home and ease control of home-entrance and equipment-usage functions.

Literature on surveillance video analysis Most surveillance systems have focused on understanding the events through the study of trajectories and positions of persons using a-priori knowledge about the scene. The Pfinder [2] system was developed to describe a moving person in an indoor environment. The VSAM [3] system can monitor activities over various scenarios, using multiple cameras which are connected as a network. The real-time visual surveillance system W4 [4] employs the combined techniques of shape analysis and body tracking, and models different appearances of a person. [2] C.R. Wren, A. Azarbayejani, T. Darrell and A.P. Pentland, “Pfinder:real-time tracking of the human body,” [3] R.T. Collins, A.J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto and O. Hasegawa, “A system for video surveillance and monitoring [4] I. Haritaoglu, D. Harwood and L. Davis, “W4: real-time surveillance of people and their activities,”

Literature on surveillance video analysis Relying on the detected trajectories of the concerned objects. As the local properties of the detected persons are missing, the developed systems lack the semantic recognition result of dynamic human activities. In this paper, we explore the combination of using trajectory and posture recognition in order to improve the semantic analysis of the human behavior.

Requirements of surveillance analysis systems The specific challenges for consumer applications are as follows: The posture and motion analysis results should have sufficient accuracy for consumer acceptance. High-processing efficiency achieving (near) real-time operation with low-cost consumer hardware. A conversion of 2-D results to a 3-D space can facilitate the analysis of special events such as burglary.

In this paper The total framework consists of four processing levels: 1. A pre-processing level including background modeling and multiple- person detection. 2. An object-based level performing trajectory estimation and posture classification. 3. An event-based level for semantic analysis. 4. A visualization level including camera calibration and 3-D scene reconstruction.

In this paper It achieves a near real-time performance (6-8 frames/second)

In this paper The location and posture of persons are visualized in a 3-D space after performing camera calibration and integrating context knowledge. The accurate and realistic reconstruction in a virtual space can significantly contribute to the scene understanding, like crime- evidence collection and healthcare behavior analysis.

Overview of proposed visual motion analysis system Pre-processing level : The background modeling and object detection. Object-based level : It performs trajectory estimation and posture classification. Event-based level : Interaction relationships are modeled to infer a multiple-person event. Visualization level : With the aim of 2D-3D mapping calibration.

Overview of proposed visual motion analysis system

Techniques for human motion analysis Pre-processing level : Multi-person detection Object-based level : Trajectory estimation Individual action recognition with CHMM Event-based level : Interaction modeling Visualization level : 3-D scene reconstruction

Background subtraction: We perform a pixel-based background subtraction. The scene model has a probability density function for each pixel separately. A pixel from a new frame is considered to be a background pixel if its new value is well described by its density function. The Gaussian Mixture Model (GMM) is employed for the background subtraction. Recognizing persons: We use the k-Nearest Neighbor (k-NN) classifier The classifier utilizes two features : area, and the ratio of the bounding box attached to each detected object. Multi-person detection

Trajectory estimation Using mean-shift algorithm : For tracking persons Based on their individual appearance model Represented as a color histogram

Trajectory estimation 1. Extracting every new person entering the scene. 2. Calculating the corresponding histogram model in the image domain. 3. In subsequent frames for tracking that person, we shift the person object to the location whose histogram is the closest to the previous frame. After the trajectory is located, we can conduct the body-based analysis at the location of the person in every frame. When the trajectory is obtained, we can also estimate the position of the persons involved in the video scene.

Individual action recognition with CHMM Posture representation HV-PCA : a new, simple and effective shape descriptor, to represent the silhouette in each frame. 1. Every detected person silhouette is adapted to an M×N pixel template in a normalization phase (M=180 and N=80). 2. We apply the horizontal and vertical projections M=180 N=80 (0)

Individual action recognition with CHMM HV-PCA : In the vertical projection : 180-D shape vector → 60*3 → 2*3 (By PCA) → 6*1(reshape) Similarly, a vector of 8×1 is reshaped from the horizontal projection P(.) indicates our part-based PCA implementation

PCA Principal component analysis (PCA) is a mathematical procedure to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.

Individual action recognition with CHMM Temporal modeling with CHMM A single-frame recognition is not sufficiently accurate when we require general motion classification. The temporal consistency is required. We use the Continuous Hidden Markov Model (CHMM) with left- right topology [12]. [12] L.R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,”

Individual action recognition with CHMM Suppose a CHMM has E states F output symbols It is fully specified by the triplet The E*E-state transition matrix A The E*F -state output probability matrix B is defined as The initial state distribution vector is specified as

Individual action recognition with CHMM Assign a CHMM model to each of the predefined posture types for the observed human body. Using the Baum-Welch algorithm to train each CHMM. So the triplet λ is obtained for each model. An observation sequence Calculate Recognize the posture class as being the one that is represented by the maximum probable model : (K=5, for the types : left- pointing, right-pointing, squatting, raising hands overhead, and lying)

Interaction Modeling In multi-person events, the event analysis is achieved by understanding the interactions between people. The events are rely on the temporal order and relationship of their sub-events (the individual posture).

Interaction Modeling To represent temporal relationships of sub-events: Temporal relationships TR={after, meets, during, finishes, overlaps, equal, starts} We can apply the heuristic rules to understand the scene. i.e. in robbery detection, the posture ‘’pointing’’ is a key reference posture.

Interaction Modeling

3-D Scene Reconstruction We want to implement the 2D-3D mapping. It is useful for scene understanding. Camera calibration: Since both the ground and the displayed image are planar, the mapping between them is a homography. p=Hp'

3-D Scene Reconstruction In our previous work [11], we have developed an automatic algorithm to establish the homography mapping for analyzing a tennis video. We manually put four white lines forming a rectangular on the ground. We have measured the length of each line in the real world, thereby defining their coordinates in the real-world domain. After performing the mapping, it plays a useful role in the crime- scene analysis, data retrieval and evidence collection. [11] J. Han, D. Farin, P.H.N. de With and W. Lao, “Real-time video content analysis tool for consumer media storage system,”

3-D Scene Reconstruction

Experimental results Training:10 video sequences, containing various single/multi-person motion (15 frames/s). Testing:15 similar sequences. Result: Person detection: 98% accuracy rate Person tracking : 95% detection rate

Experimental results The robbery detection rate is 90% in our captured simulated- robbery video sequences (in total 10 sequences) Our system is efficient, achieving a near real-time performance (6-8 frames/second for 640*480 resolution (VGA), with a P-IV 3-GHz PC)

Experimental results