Automated detection of unusual events on stairs
This paper presents a method for automatically detecting unusual human events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We compute two sets of features from a video of a person descending a stairwell. The first set of features are the foot positions and velocities. We track both feet using a mixed state particle filter with an appearance model based on histograms of oriented gradients. We compute expected (most likely) foot positions given the state of the filter at each frame. The second set of features are the parameters of the mean optical flow over a foreground region. Our final classification system inputs these two sets of features into a hidden Markov model (HMM) to analyse the spatio-temporal progression of the stair descent. A single HMM is trained on sequences of normal stair use, and a threshold on sequence likelihoods is used to detect unusual events in new data. We demonstrate our system on a data set with five people descending a set of stairs in a laboratory environment. We show how our system can successfully detect nearly all anomalous events, with a low false positive rate. We discuss limitations and suggest improvements to the system.
stairs particle filter hidden markov model human motion analysis gait analysis anomaly detection event recognition bayesian tracking automated video analysis machine learning biomedical analysis