Craig Henderson

Final Year PhD Research Student exploring Computer Vision and Machine Learning using C++11.

My research interests are in 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 to locate distinctive clothing, bags or markings such as tatoos.

Older projects, publications and research can be found here

Publications

Symmetric Stability of Low Level Feature Detectors

with Professor Ebroul Izquierdo

Pattern Recognition Letters, volume 78 pp. 36-40

DOI: 10.1016/j.patrec.2016.03.027 Download PDF

Abstract

We investigate the capability of low level feature detectors to consistently define feature keypoints in an image and its horizontally reflected (mirrored) image. It is our assertion that this consistency is a useful attribute of a feature detector and should be considered in assessing the robustness of a feature detector. We test ten of the most popular detectors using a popular dataset of 8,677 images. We define a set of error measurements to help us to understand the invariance in keypoint position, size and angle of orientation, and we use SIFT descriptors extracted from the keypoints to measure the consistency of extracted feature descriptors. We conclude that the FAST and STAR detectors are perfectly invariant to bilateral symmetry, Good Features to Track and the Harris Corner detector produce consistent keypoints that can be matched using feature descriptors, and others vary in their invariance. SIFT is the least invariant of all the detectors that we test.

Multi-scale reflection invariance

with Professor Ebroul Izquierdo

Accepted for oral presentation, 13-15 July 2016, London

SAI Computing conference, (SAI 2016)

Abstract

In this position paper, we consider the state of computer vision research with respect to invariance to the horizontal orientation of an image what we term reflection invariance. We describe why we consider reflection invariance to be an important property and provide evidence where the absence of this invariance produces surprising inconsistencies in state-of-the-art systems. We demonstrate inconsistencies in methods of object detection and scene classification when they are presented with images and the horizontal mirror of those images. Finally, we examine where some of the invariances are exhibited in feature detection and descriptors, and make a case for future consideration of reflection invariance as a measure of quality in computer vision algorithms.

Rethinking Random Hough Forests for video database indexing and pattern search

with Professor Ebroul Izquierdo

4th International Conference on Computational Visual Media

Cardiff, 6-8 April 2016

DOI: 10.1007/s41095-016-0039-3 Open Access published by Springer

Abstract

Hough Forests have demonstrated effective performance in object detection tasks, which has potential to translate to exciting opportunities in pattern search. However, current systems are incompatible with the scalability and performance requirements of an interactive visual search. In this paper, we pursue this potential by rethinking the methods of Hough Forest training and regression to devise a system that is synonymous with a search index that can yield pattern search results in near real-time. The system performs well on simple pattern detection, demonstrating the concept is sound. However, detection of patterns in complex and crowded street-scenes is more challenging. Some success is demonstrated in such videos, and describe future work that will address some of the key questions arising from our work to date.

Feature correspondence in low quality CCTV videos

with Professor Ebroul Izquierdo

Book Chapter in
Emerging Trends and Advanced Technologies for Computational Intelligence
in preparation 3rd September 2015

ISBN: 978-3-319-33351-9     Springer International Publishing

DOI: 10.1007/978-3-319-33353-3

Abstract

Closed-circuit television cameras are used extensively to monitor streets for the security of the public. Whether passively recording day-to-day life, or actively monitoring a developing situation such as public disorder, the videos recorded have proven invaluable to police forces world wide to trace suspects and victims alike. The volume of video produced from the array of camera covering even a small area is large, and growing in modern society, and post-event analysis of collected video is a time consuming problem for police forces that is increasing. Automated computer vision analysis is desirable, but current systems are unable to reliably process videos from CCTV cameras. The video quality is low, and computer vision algorithms are unable to perform sufficiently to achieve usable results. In this chapter, we describe some of the reasons for the failure of contemporary algorithms and focus on the fundamental task of feature correspondence between frames of video - a well-studied and often considered solved problem in high quality videos, but still a challenge in low quality imagery. We present solutions to some of the problems that we acknowledge, and provide a comprehensive analysis where we demonstrate feature matching using a 138-dimensional descriptor that improves the matching performance of a state-of-the-art 384-dimension colour descriptor with just 36% of the storage requirements.

Minimal Hough Forest training for pattern detection in Query by Example video search

with Professor Ebroul Izquierdo

22nd International Conference on Systems, Signals and Image Processing

London, 10-12 September 2015

DOI: 10.1109/IWSSIP.2015.7314179

Abstract

We assess configuration parameters of a Hough Forest and their impact on performance to achieve good pattern detection in images without need or opportunity for large-scale training. Our motivation is the application of Query-by-Example video search where a region of interest in a video frame image is selected by the user as a query sample. We describe a method to discover training images from the single query region and assess the impact of tuning standard Hough Forest parameters on the application of pattern detection using a very small training set of data. We introduce a novel method by which to improve training performance and precision in pattern detection by adaptive selection of the patch size and the number of patches used. We show results using challenging street-scene videos demonstrating our method to improve precision and performance over static general-purpose Hough Forest parameters used in the literature.

Robust feature matching in long-running poor quality videos

with Professor Ebroul Izquierdo

IEEE Transactions on Circuits and Systems for Video Technology 26(6) pp. 1161 - 1174

DOI: 10.1109/TCSVT.2015.2441411

Abstract

We describe a methodology that is designed to match key point and region-based features in real-world images, acquired from long-running security cameras with no control over the environment. We detect frame duplication and images from static scenes that have no activity to prevent processing saliently identical images, and describe a novel blur sensitive feature detection method, a combinatorial feature descriptor and a 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 that are low quality and acquired in uncontrolled conditions with visual distortions caused by weather, crowded scenes, emergency lighting or the high angle of the camera mounting. We demonstrate an improvement in accuracy of matching key points between images compared with state-of-the-art feature descriptors. 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. We demonstrate feature matching using a 138-dimensional descriptor that improves the matching performance of a state-of-the-art 384-dimension colour descriptor with just 36% of the storage requirements.

Feature matches

Robust Feature Matching in the Wild

with Professor Ebroul Izquierdo

Science and Information Conference

London, 25-30th July 2015, pp 628-637      Winner, Best Paper Award

DOI: 10.1109/SAI.2015.7237208

Abstract and BibTex

Finding corresponding key points in images from security camera videos is challenging. Images are generally low quality and acquired in uncontrolled conditions with visual distortions caused by weather, crowded scenes, emergency lighting or the high angle of the camera mounting. We describe a methodology to match features between images that performs especially well with real-world images. We introduce a novel \emph{blur sensitive feature detection} method, a combinatorial feature descriptor and a 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. We demonstrate feature matching using a 138-dimensional descriptor that improves the matching performance of a state-of-the-art 384-dimension colour descriptor with just 40% of the storage requirements.

@inproceedings{Henderson2015,
address = {London},
author = {Henderson, Craig and Izquierdo, Ebroul},
booktitle = {Science and Information Conference},
title = {{Robust Feature Matching in the Wild}},
year = {2015}
}

On the impurity of street-scene video footage

with Dr Saverio G. Blasi and Faranak Sobhani

6th International Conference on Imaging for Crime Detection and Prevention ICDP-2015

London, 15-17th July 2015

DOI: 10.1049/ic.2015.0119

Abstract

The Metropolitan Police in London have found that the opportunity to use computer vision technology in the analysis of real-world street-scene video is severely limited because of the practical constraints in the variety and poor quality of videos available to them. Consequently, in a large criminal investigation, police forces employ numerous officers and volunteers to watch many hours of camera footage to locate, identify and trace the movements of suspects, victims, witnesses, luggage and other inanimate objects. Their goal is to piece together a story of events leading up to an incident, and to determine what happened afterwards. In this paper, we present the technical challenges facing researchers in developing computer vision technique to process from the wild street-scene videos.

Posters

On the impurity of street-scene video footage (poster)

with Dr Saverio G. Blasi and Faranak Sobhani

6th International Conference on imaging for Crime Prevention and Detection ICDP-2015

London, 15th-17nd July 2015

Poster summary of our tutorial presentation.

Look this way (poster)

with Professor Ebroul Izquierdo

ViiHM Workshop, Bath, 1st-2nd July 2015
Visual Image Interpretation in Humans and Machines Workshop

A workshop poster presenting our research in Reflection Invariance.

Boosting feature matching accuracy with colour information (poster)

with Professor Ebroul Izquierdo

BMVA Summer School, Swansea, May 2014

ViiHM Workshop, Stratford-upon-Avon, September 2014
Visual Image Interpretation in Humans and Machines Workshop

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