Multi-regime analysis for computer vision-based traffic surveillance using a change-point detection algorithm

TL;DR

This paper introduces a multi-regime analysis approach for traffic surveillance that uses change-point detection to adapt to varying ambient conditions, significantly improving traffic density measurement accuracy.

Contribution

It presents a novel online change-point detection method combined with autoencoder-based feature reduction for effective multi-regime traffic analysis.

Findings

Multi-regime analysis outperforms integrated models in accuracy.

Change-point detection effectively identifies ambient condition shifts.

Autoencoder reduces input image dimensions for efficient processing.

Abstract

As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.