Enhancing Event Detection in Video Using Robust Background and Quality Modeling

摘要

Automated event recognition in video data has numerous practical applications for security and transportation. The ability to recognize events in practice depends on precisely detecting and tracking objects of interest in the video data. Numerous factors, such as lighting, weather, camera placement, scene complexity, and data compression can degrade the performance of automated algorithms. As a preprocessing step, developing a set of robust background models can substantially improve system performance. Our object detection and tracking algorithms estimate the object position and attributes within the context of this model to provide more reliable event recognition under challenging conditions. We present an approach to robustly modeling the background as a function of the data acquisition conditions. One element of this approach is automated assessment of the image quality which informs the choice of which background model to use for a given video stream. The video quality model rests on a suite of image metrics computed in real-time from the video, whereas the background models are constructed from historical data collected over a range of conditions. We will describe the formulation of both models. Results from a recent experiment will quantify the empirical performance for recognition of events of interest.