Abstract

This paper designs an intelligent video surveillance system based on the particle filter. In the design, the adaptive Gaussian mixture model is applied to construct the background model. Utilizing the Gaussian mixture background model, the moving objects can be detected by background subtraction. For the moving objects appearing in the margin of the video frame, it is considered as a new unit (person). For the new considered unit, a new particle filter is established and designated to track the new unit. Once the tracked unit leaves the video frame, the corresponding particle filter will be terminated. Moreover, the Kalman filter is applied to track the units when they are occluded. By tracking the units in the video frame, we can obtain some important information, e.g. the number of persons in the area (or having been in the area), hot spots, etc.

Highlights

  • Video surveillance systems are often utilized at some specific places such as exits, entrances, parking lots, convenient stores, etc. for management

  • Particle filters and Kalman filters are employed for the purpose of tracking objects in intelligent video surveillance (IVS) systems [6,7,8,9,10]

  • The designed IVS system can gather the information of the number of persons being in the area, the number of persons having been in the area, and hot spots of the area

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Summary

Introduction

Video surveillance systems are often utilized at some specific places such as exits, entrances, parking lots, convenient stores, etc. for management. According to statistics, one security guard can only watch four monitors at the same time, and the concentration can last only for 10 minutes such that more than 50% of key information is lost. The designed IVS system can gather the information of the number of persons being in the area, the number of persons having been in the area, and hot spots (places of more than usual interest, activity, or popularity) of the area. The Gaussian mixture background model is utilized to detect moving objects by background subtraction in the designed IVS system. Information of the number of persons in the area (having been in the area) and hot spots is gathered by tracking persons in the video frame. By hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience

Adaptive Gaussian mixture model
Particle filter
Kalman filter
IVS system design
Particle filter for tracking units
Kalman filter for correcting and estimating positions
Experiment results
Findings
Conclusion

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