Improved image analysis methodology for detecting changes in evidence positioning at crime scenes

This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography. © 2019 IEEE.E

Effective design, configuration, and use of digital CCTV

It is estimated that there are five million CCTV cameras in use today. CCTV is used by a wide range of organisations and for an increasing number of purposes. Despite this, there has been little research to establish whether these systems are fit for purpose. This thesis takes a socio-technical approach to determine whether CCTV is effective, and if not, how it could be made more effective. Humancomputer interaction (HCI) knowledge and methods have been applied to improve this understanding and what is needed to make CCTV effective; this was achieved in an extensive field study and two experiments. In Study 1, contextual inquiry was used to identify the security goals, tasks, technology and factors which affected operator performance and the causes at 14 security control rooms. The findings revealed a number of factors which interfered with task performance, such as: poor camera positioning, ineffective workstation setups, difficulty in locating scenes, and the use of low-quality CCTV recordings. The impact of different levels of video quality on identification and detection performance was assessed in two experiments using a task-focused methodology. In Study 2, 80 participants identified 64 face images taken from four spatially compressed video conditions (32, 52, 72, and 92 Kbps). At a bit rate quality of 52 Kbps (MPEG-4), the number of faces correctly identified reached significance. In Study 3, 80 participants each detected 32 events from four frame rate CCTV video conditions (1, 5, 8, and 12 fps). Below 8 frames per second, correct detections and task confidence ratings decreased significantly. These field and empirical research findings are presented in a framework using a typical CCTV deployment scenario, which has been validated through an expert review. The contributions and limitations of this thesis are reviewed, and suggestions for how the framework should be further developed are provided

A survey on passive digital video forgery detection techniques

Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed

Time-lapse geophysical investigations over a simulated urban clandestine grave

A simulated clandestine shallow grave was created within a heterogeneous, made-ground, urban environment where a clothed, plastic resin, human skeleton, animal products, and physiological saline were placed in anatomically correct positions and re-covered to ground level. A series of repeat (time-lapse), near-surface geophysical surveys were undertaken: (1) prior to burial (to act as control), (2) 1 month, and (3) 3 months post-burial. A range of different geophysical techniques was employed including: bulk ground resistivity and conductivity, fluxgate gradiometry and high-frequency ground penetrating radar (GPR), soil magnetic susceptibility, electrical resistivity tomography (ERT), and self potential (SP). Bulk ground resistivity and SP proved optimal for initial grave location whilst ERT profiles and GPR horizontal "time-slices" showed the best spatial resolutions. Research suggests that in complex urban made-ground environments, initial resistivity surveys be collected before GPR and ERT follow-up surveys are collected over the identified geophysical anomalies

A computer vision system for detecting and analysing critical events in cities

Whether for commuting or leisure, cycling is a growing transport mode in many cities worldwide. However, it is still perceived as a dangerous activity. Although serious incidents related to cycling leading to major injuries are rare, the fear of getting hit or falling hinders the expansion of cycling as a major transport mode. Indeed, it has been shown that focusing on serious injuries only touches the tip of the iceberg. Near miss data can provide much more information about potential problems and how to avoid risky situations that may lead to serious incidents. Unfortunately, there is a gap in the knowledge in identifying and analysing near misses. This hinders drawing statistically significant conclusions to provide measures for the built-environment that ensure a safer environment for people on bikes. In this research, we develop a method to detect and analyse near misses and their risk factors using artificial intelligence. This is accomplished by analysing video streams linked to near miss incidents within a novel framework relying on deep learning and computer vision. This framework automatically detects near misses and extracts their risk factors from video streams before analysing their statistical significance. It also provides practical solutions implemented in a camera with embedded AI (URBAN-i Box) and a cloud-based service (URBAN-i Cloud) to tackle the stated issue in the real-world settings for use by researchers, policy-makers, or citizens. The research aims to provide human-centred evidence that may enable policy-makers and planners to provide a safer built environment for cycling in London, or elsewhere. More broadly, this research aims to contribute to the scientific literature with the theoretical and empirical foundations of a computer vision system that can be utilised for detecting and analysing other critical events in a complex environment. Such a system can be applied to a wide range of events, such as traffic incidents, crime or overcrowding

The use of low cost virtual reality and digital technology to aid forensic scene interpretation and recording

© Cranfield University 2005. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner.Crime scenes are often short lived and the opportunities must not be lost in acquiring sufficient information before the scene is disturbed. With the growth in information technology (IT) in many other scientific fields, there are also substantial opportunities for IT in the area of forensic science. The thesis sought to explore means by which IT can assist and benefit the ways that forensic information can be illustrated and elucidated in a logical manner. The central research hypothesis considers that through the utilisation of low cost IT, the visual presentation of information will be of significant benefit to forensic science in particular for the recoding of crime scenes and its presentation in court. The research hypothesis was addressed by first exploring the current crime scene documentation techniques; their strengths and weaknesses, giving indication to the possible niche that technology could occupy within forensic science. The underlying principles of panoramic technology were examined, highlighting its ability to express spatial information efficiently. Through literature review and case studies, the current status of the technology within the forensic community and courtrooms was also explored to gauge its possible acceptance as a forensic tool. This led to the construction of a low cost semi-automated imaging system capable of capturing the necessary images for the formation of a panorama. This provides the ability to pan around; effectively placing the viewer at the crime scene. Evaluation and analysis involving forensic personnel was performed to assess the capabilities and effectiveness of the imaging system as a forensic tool. The imaging system was found to enhance the repertoire of techniques available for crime scene documentation; possessing sufficient capabilities and benefits to warrant its use within the area of forensics, thereby supporting the central hypothesis

Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions