A holistic multi-scale approach to using 3D scanning technology in accident reconstruction

Three-dimensional scanning and documentation methods are becoming increasingly employed by law enforcement personnel for crime scene and accident scene recording. Three-dimensional documentation of the victim’s body in such cases is also increasingly used as the field of forensic radiology and imaging is expanding rapidly. These scanning technologies enable a more complete and detailed documentation than standard autopsy. This was used to examine a fatal pedestrian-vehicle collision where the pedestrian was killed by a van whilst crossing the road. Two competing scenarios were considered for the vehicle speed calculation: the pedestrian being projected forward by the impact or the pedestrian being carried on the vehicle’s bonnet. In order to assist with this, the impact area of the accident vehicle was scanned using laser surface scanning, the victim was scanned using postmortem CT and micro-CT and the data sets were combined to virtually match features of the vehicle to injuries on the victim. Micro-CT revealed additional injuries not previously detected, lending support to the pedestrian-carry theory

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

Image enhancement from a stabilised video sequence

The aim of video stabilisation is to create a new video sequence where the motions (i.e. rotations, translations) and scale differences between frames (or parts of a frame) have effectively been removed. These stabilisation effects can be obtained via digital video processing techniques which use the information extracted from the video sequence itself, with no need for additional hardware or knowledge about camera physical motion. A video sequence usually contains a large overlap between successive frames, and regions of the same scene are sampled at different positions. In this paper, this multiple sampling is combined to achieve images with a higher spatial resolution. Higher resolution imagery play an important role in assisting in the identification of people, vehicles, structures or objects of interest captured by surveillance cameras or by video cameras used in face recognition, traffic monitoring, traffic law reinforcement, driver assistance and automatic vehicle guidance systems