Abstract:
To evaluate the crowd densities in video scene under different constraints. For the crowded video analysis
robust foreground detection methods are required to differentiate between moving or static foreground objects and
static or dynamic background. Large number of foreground segmentation or motion segmentation approaches are
available but only few can handle the various constraints like illumination variations, dynamic background partial or
high level of occlusions. Method: We have proposed a modified Gaussian mixture model using adaptive thresholding. The
proposed approach is implemented in MATLAB. Findings: Our proposed approach analyze all the aspects of the various
backgrounds and foregrounds modelling and then compared their critical performance in terms of the PR curves and
miss rate. The performance evaluation demonstrates considerable improvements in miss rate compared to traditional
approaches. Our proposed method also shows significant improvements in Multi Object Detection and in Tracking Accuracy.
Application: Our proposed approach analyzes the crowded scenes, especially handles outdoor environment. Optimized
model parameters and adaptive thresholding makes it more robust to handle varying light conditions and partial occlusions.
