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In this paper we present a realtime computer vision approach to recognizing human movements based on pat terns of motion The underlying representation is a Motion History Image MHI which is characterized by multiple histograms of the local motion orientations The approach is adapted to accommodate movements of dierent durations by using a simple iterative technique Quantitative results are given showing discrimination between dierent human movements using the approach An extension addressing occlusion and distractor motion is also presented within this framework

The recognition of human motion and action using computer vision has widespread interest ranging from surveillance applications to entertainment systems Be ing able to recognize the presence of human motion is desirable because every little change or movement in the environment may not be consequential Monitoring ap plications for instance may wish to signal only when a person is seen in a particular area perhaps inside a dangerous or secure area Thus only those motions be longing to human activity are of importance In the entertainment domain an increasing interest in peo ple watching systems is growing Here the systems watch for the gestures made by participants which con troldrive the program or interaction

Thus one requirement or demand of such machine vision sys tems is their ability to perform in realtime It would not be of much use for the monitoring system to report that a person entered into a dangerous area an hour after the fact Also systems incorporating human ges tures for input must recognize and respond quickly to the user without noticeable lag to give a sense of im mersion and actual control The quickness of response is paramount In this paper we present a realtime computer vision approach to recognizing human movements In earlier work we described a representation of movement known as a Motion History Image MHI The MHI is a compact representation of temporal movement and is simple to compute In this paper we present a new method for recognizing movement which relies on local ized regions of motion derived from the MHI By gath ering and matching multiple overlapping histograms of the motion orientations from the MHI we oer a real time solution to recognizing various human movements The remainder of this paper rst examines the re lated research for which this work has been based Sec tion Next we present the approach of using motion histograms Section This section is subdivided into discussions on the underlying representation Section

the calculation of the motion orientations Sec tion

and the histogram generation Section

We then present a simple recognition method Section along with some quantitative results Section A method for handling variablelength movements is also described Section We then address the notions of occlusion and distractor motions within an extension of this framework Section Lastly we conclude with a brief summary of the approach Section

In this paper we presented a realtime computer vision approach to recognizing human movements based on patterns of motion The underlying representation is a Motion History Image MHI which is characterized by multiple overlapping histograms of the motion orienta tions These histograms separate and localize regions of motion for a better description of the movement Quan titative results show that the method can easily discrim inate between dierent human movements and is ex tendible to variable length motions Occlusion and dis tractor motions are also addressable within this frame work

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