Abstract
We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.
Highlights
Among all the objects involved in a transportation system, pedestrians are the major participants
Pedestrian count estimation plays an important role in public security, traffic control, and other aspects
This study presents a pedestrian count estimation method that considers the spatial distribution characteristics of local features
Summary
Among all the objects involved in a transportation system, pedestrians are the major participants. Keywords Pedestrian count estimation, global feature representation, support vector regression, histograms of multi-scale block local binary pattern Direction, or angle of the camera changes, the camera should be recalibrated.[27] In this study, we present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
