While Convolutional Neural Network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrian from surveillance images is still challenging.The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem,we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians,thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged and the super-resolution sub-network can recover some useful detailed information for the subsequent classification.Based on HOG+LUV and JCS-Net, multi-layer channel features(MCF) are constructed to train the detector. Experimental results on the Cal tech pedestrian data set and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state of-the-art performance.
To View the Abstract Contents
Now it is Your Time to Shine.
Great careers Start Here.
We Guide you to Every Step
Success! You're Awesome
Thank you for filling out your information!
We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.
To know more details Call 900 31 31 555
The WISEN Team