Learning Behavioural Context

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Bernoulli filter for joint detection and tracking of an extended object in clutter

The problem is joint detection and tracking of a non-point or extended moving object, characterised by multiple feature points, which can result in detections. Owing to imperfect detection, only some of the feature points are detected and in addition, false alarms [or clutter] can also be present. Standard tracking techniques assume point objects, that is at most one detection per object, and hence are not adequate for this problem. This study presents a principled theoretical solution in the form of the Bayes filter, referred to as the Bernoulli filter for an extended object. The derivation follows the random set filtering framework introduced by Mahler. The filter is implemented approximately as a particle filter and subsequently applied both to simulated data and a real video sequence

Particle filter to track multiple people for visual surveillance

A particle filter (PF) has been recently proposed to detect and track colour objects in video. This study presents an adaptation of the PF to track people in surveillance video. Detection is based on automated background modelling rather than a manually generated object colour model. Furthermore, a labelling method is proposed to create tracks of objects through the scene, rather than unconnected detections. A methodical comparison between the new PF tracking method and two other multi-object trackers is presented on the PETS 2004 benchmark data set. The PF tracker gives significantly fewer false alarms owing to explicit modelling of the object birth and death processes, while maintaining a good detection rate. © 2011 The Institution of Engineering and Technology

Abstract Face distributions in similarity space under varying head pose q

Real-time identity-independent estimation of head pose from prototype images is a perplexing task requiring pose-invariant face detection. The problem is exacerbated by changes in illumination, identity and facial position. We approach the problem using a view-based statistical learning technique based on similarity of images to prototypes. For this method to be effective, facial images must be transformed in such a way as to emphasise differences in pose while suppressing differences in identity. We investigate appropriate transformations for use with a similarity-to-prototypes philosophy. The results show that orientation-selective Gabor ®lters enhance differences in pose and that different ®lter orientations are optimal at different poses. In contrast, principal component analysis �PCA) was found to provide an identity-invariant representation in which similarities can be calculated more robustly. We also investigate the angular resolution at which pose changes can be resolved using our methods. An angular resolution of 108 was found to be suf®ciently discriminable at some poses but not at others, while 20

Performance Evaluation of Random Set Based Pedestrian Tracking Algorithms

The paper evaluates the error performance of three random finite set based multi-object trackers in the context of pedestrian video tracking. The evaluation is carried out using a publicly available video dataset of 4500 frames (town centre street) for which the ground truth is available. The input to all pedestrian tracking algorithms is an identical set of head and body detections, obtained using the Histogram of Oriented Gradients (HOG) detector. Head and body detections are unreliable in the sense that the probability of detection is low and false detections are non-uniformly distributed. The tracking error is measured using the recently proposed OSPA metric for tracks (OSPA-T), adopted as the only known mathematically rigorous metric for measuring the distance between two sets of tracks. The paper presents the correct proof of the triangle inequality for the OSPA-T. A comparative analysis is presented under various conditions

Semantic labeling of aerial and satellite imagery

Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery.The combination of these factors leads to a semantic labeling frameworkwhich outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to 88% on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.Sakrapee Paisitkriangkrai, Jamie Sherrah, Pranam Janney and Anton van den Henge

Effective semantic pixel labelling with convolutional networks and Conditional Random Fields

Large amounts of available training data and increasing computing power have led to the recent success of deep convolutional neural networks (CNN) on a large number of applications. In this paper, we propose an effective semantic pixel labelling using CNN features, hand-crafted features and Conditional Random Fields (CRFs). Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. The CRF infers a labelling that smooths regions while respecting the edges present in the imagery. The method is applied to the ISPRS 2D semantic labelling challenge dataset with competitive classification accuracy.Sakrapee Paisitkriangkrai, Jamie Sherrah, Pranam Janney, and Anton Van Den Henge

Interpretation of group behaviour in visually mediated interaction

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