Pedestrian-Detection-Project

Project Video and Paper:

Project Video

Project Paper

Project Goal:

The goal of this project is to explore how better feature representation and various visual cues can be utilized to improve detection quality.

Specifically, this project targets the fascinating and meaningful real world problem "pedestrian detection" as a test case. Using current state of the art pedestrian detector "SquaresChnFtrs" as a baseline, I leverage two approaches to increase detection accuracy. Expand 10 HOG+LUV channels into 20 channels by using DCT (discrete cosine transform); Encode the optical flow using SDt features (image difference between current frame T and coarsely aligned T-4 and T-8).

Note that this project is largely to reproduce observations/discovery in “Benenson etc., 2014 ECCV” paper. The DCT method is expected to yield 3.53% miss rate improvement, and the optical flow method is expected to yield 4.47% improvement.

What Has Been Done:

The project started in mid-November 2014, up to now, below is achieved::

1. Got the baseline detector up and running
2. Got baseline miss rate
3. Implemented the new baseline + DCT pedestrian detector.
4. Cross verified that DCT algorithm CUDA implementation in the new detector is correct. Codes here

What's Next:

Implement the baseline + optical flow

Current Major Issues:

Refer here for a complete list of issues and corresponding updates in this project.