Craig Henderson

PhD Research Student exploring techniques in Computer Vision, Machine Learning using C++11

My research interests centre around object tracking and retrieval in video and temporal still images, particularly in low frame-rate and multi-angle scene imaging such as CCTV. I am experimenting with object search and re-identification with a goal to locating offenders in previous footage by way of distinctive clothing or markings such as tatoos.

Older projects, publications and research can be found here

Publications

Robust Feature Matching in the Wild

with Professor Ebroul Izquierdo

under review

Image Quality at Distance

We describe a methodology to match features between images that performs especially well with real-world images. Using our method, we demonstrate feature matching using a 138-dimensional descriptor that improves on matching a state-of-the-art 384-dimension colour descriptor, and our own enhanced 394-dimensional descriptor, with 40% of the storage requirements.

We introduce a blur sensitive feature detection method and a novel combinatorial feature descriptor and distance calculation that efficiently unites texture and colour attributes to discriminate feature correspondence in low quality images.

Our methods are tested by performing key point matching on real-world security images such as outdoor CCTV videos, and we demonstrate an improvement in the ability to match features between images compared with the standard feature descriptors extracted from the same set of feature points. We use key point features from Harris Corners, SIFT, SURF, BRISK and FAST as well as MSER and MSCR region detectors to provide a comprehensive analysis of our generic method.

Large-scale forensic analysis of security images and videos

with Professor Ebroul Izquierdo

6th Doctoral Consortium, BMVC 2014, Nottingham, 5th September 2014



Our research is concerned with the practical application of computer vision in the forensic analysis of security images and videos. Contemporary literature make use of high-definition images and Hollywood feature films in their datasets, and there is little or no assessment of algorithms' performance using poor quality images with variable frame rates and uncontrolled lighting conditions such as security video.

Work so far has produced a methodology for matching features across low quality images that yields an improved results over existing feature matching techniques. Future work will involve innovation in search and retrieval online machine learning to train models from unlabelled data, and segmentation of one-shot videos to aid computer and human analysis of long-running video sequences. We are motivated to produce an integrated system for police investigators to use a query-by-example search and retrieval system with relevance feedback and machine learning to incrementally discover evidence in criminal investigations.

@inproceedings{Henderson2014,
address = {Nottingham},
author = {Henderson, Craig and Izquierdo, Ebroul},
booktitle = {6th Doctoral Consortium at BMVC 2014},
title = {{Large-scale forensic analysis of security images and videos}},
year = {2014}
}
                            

Boosting feature matching accuracy with colour information PDF (poster)

with Professor Ebroul Izquierdo

BMVA Summer School, Swansea, May 2014

ViiHM Workshop, Stratford-upon-Avon, September 2014



We boost the performance of discriminative matching of features between colour images, with

  • a space-efficient and generic extension for any feature descriptor
  • a novel distance measure calculation