Preattentive and attentive detection of humans in widefield scenes

Abstract. We address the problem of localizing and obtaining high-resolution footage of the people present in a scene. We propose a biologically-inspired solution combining pre-attentive, low-resolution sensing for detection with shiftable, high-resolution, attentive sensing for confirmation and further analysis. The detection problem is made difficult by the unconstrained nature of realistic environments and human behaviour, and the low resolution of pre-attentive sensing. Analysis of human peripheral vision suggests a solution based on integration of relatively simple but complementary cues. We develop a Bayesian approach involving layered probabilistic modeling and spatial integration using a flexible norm that maximizes the statistical power of both dense and sparse cues. We compare the statistical power of several cues and demonstrate the advantage of cue integration. We evaluate the Bayesian cue integration method for human detection on a labelled surveillance database and find that it outperforms several competing methods based on conjunctive combinations of classifiers (e.g., Adaboost). We have developed a real-time version of our pre-attentive human activity sensor that generates saccadic targets for an attentive foveated vision system. Output from high-resolution attentive detection algorithms and gaze state parameters are fed back as statistical priors and combined with pre-attentive cues to determine saccadic behaviour. The result is a closed-loop system that fixates faces over a 130 deg field of view, allowing high-resolution capture of facial video over a large dynamic scene. 1

Fast Semi-dense Surface Reconstruction from Stereoscopic Video in Laparoscopic Surgery

Liver resection is the main curative option for liver metastases. While this offers a 5-year survival rate of 50%, only about 20% of all patients are suitable for laparoscopic resection and thus being able to take advantage of minimally invasive surgery. One underlying difficulty is the establishment of a safe resection margin while avoiding critical structures. Intra-operative registration of patient scan data may provide a solution. However, this relies on fast and accurate reconstruction methods to obtain the current shape of the liver. Therefore, this paper presents a method for high-resolution stereoscopic surface reconstruction at interactive rates. To this end, a feature-matching propagation method is adapted to multi-resolution processing to enable parallelisation, remove global synchronisation issues and hence become amenable to a GPU-based implementation. Experiments are conducted on a planar target for reconstruction noise estimation and a visually realistic silicone liver phantom. Results highlight an average reconstruction error of 0.6 mm on the planar target, 2.4-5.7 mm on the phantom and processing times averaging around 370 milliseconds for input images of size 1920 x 540