Publications by Simone Calderara

We propose a formulation of people tracking problem as a Transductive Learning (TL) problem. TL is an effective semi-supervised learning technique by which many classification problems have been recently reinterpreted as learning labels from incomplete datasets. In our proposal the joint exploitation of spectral graph theory and Riemannian manifold learning tools leads to the formulation of a robust approach for appearance based tracking in Video Surveillance scenarios. The key advantage of the presented method is a continuously updated model of the tracked target, used in the TL process, that allows to on-line learn the target visual appearance and consequently to improve the tracker accuracy. Experiments on public datasets show an encouraging advancement over alternative state-of the-art techniques.

Video surveillance is becoming the technology of choice for monitoring crowded areas for security threats. While video provides ample information for human inspectors, there is a great need for robust automated techniques that can efficiently detect anomalous behavior in streaming video from single ormultiple cameras. In this work we synergistically combine two state-of-the-art methodologies. The rst is the ability to track and label single person trajectories in a crowded area using multiple video cameras, and the second is a new class of novelty detection algorithms based on spectral analysis of graphs. By representing the trajectories as sequences of transitions betweennodes in a graph, shared individual trajectories capture only a small subspace of the possible trajectories on the graph. This subspace is characterized by large connected components of the graph, which are spanned by the eigenvectors with the low eigenvalues of the graph Laplacian matrix. Using this technique, we develop robust invariant distance measures for detectinganomalous trajectories, and demonstrate their application on realvideo data.

Relevance feedback is a widely adopted approach to improve content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. Recently, combination of feature space warping and query point movement techniques has been proposed in contrast to learning based approaches, showing good performance under dierent data distributions. In this paper we propose to merge feature space warping and transductive learning, in order to benet from both the ability of adapting data to the user hints and the information coming from unlabeled samples. Experimental results on an image retrieval task reveal signicant performance improvements from the proposed method.

Large sets of visual data are now available both, in real time andoff line, at time of investigation in multimedia forensics, however passive querying systems often encounter difficulties in retrieving significant results. In this paper we propose an iterativeactive querying system for video surveillance and forensic applications based on the continuous interaction between the userand the system. The positive and negative user feedbacks areexploited as the input of a graph based transductive procedurefor iteratively refining the initial query results. Experimentsare shown using people trajectories and people appearance asdistance metrics.

This paper presents a method for recognising human actions bytracking body parts without using artificial markers. A sophisticated appearance-based tracking able to cope with occlusions is exploited to extract a probability map for each moving object. A segmentation technique based on mixture of Gaussians (MoG) is then employed to extract and track significantpoints on this map, corresponding to significant regions on the human silhouette. The evolution of the mixture in time is analysed by transforming it in a sequence of symbols (corresponding to a MoG). The similarity between actions is computed by applying global alignment and dynamic programming techniques to the corresponding sequences and using a variational approximation of the Kullback-Leibler divergence to measure the dissimilarity between two MoGs. Experiments on publicly available datasets and comparison with existing methods are provided.

People trajectory analysis is a recurrent task inmany pattern recognition applications, such as surveillance,behavior analysis, video annotation, and many others. In thispaper we propose a new framework for analyzing trajectoryshape, invariant to spatial shifts of the people motion in thescene. In order to cope with the noise and the uncertainty ofthe trajectory samples, we propose to describe the trajectoriesas a sequence of angles modelled by distributions of circularstatistics, i.e. a mixture of von Mises (MovM) distributions.To deal with MovM, we define a new specific EM algorithmfor estimating the parameters and derive a closed form of theBhattacharyya distance between single vM pdfs. Trajectories arethen modelled with a sequence of symbols, corresponding tothe most suitable distribution in the mixture, and comparedeach other after a global alignment procedure to cope withtrajectories of different lengths. The trajectories in the trainingset are clustered according with their shape similarity in an offlinephase, and testing trajectories are then classified with aspecific on-line EM, based on sufficient statistics. The approachis particularly suitable for classifying people trajectories in videosurveillance, searching for abnormal (i.e. infrequent) paths. Testson synthetic and real data are provided with also a completecomparison with other circular statistical and alignment methods.

Following people in different video sources is a challenging task: variations in the type of camera, in the lighting conditions, in the scene settings (e.g. crowd or occlusions) and in the point of view must be accounted. In this paper we propose a system based only on appearance information that, disregarding temporal and spatial information, can be flexibly applied on both moving and static cameras. We exploit the joint use of transductive learning and spectral properties of graph Laplacians proposing a formulation of the people tracing problem as a semi-supervised classification. The knowledge encoded in two labeled input sets of positive and negative samples of the target person and the continuous spectral update of these models allow us to obtain a robust approach for people tracing in surveillance video sequences. Experiments on publicly available datasets show satisfactory results and exhibit a good robustness in dealing with short and long term occlusions.

Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas. Many commercial smoke detection sensors exist but most of them cannot be applied in open space or outdoor scenarios. With this aim, the paper presents a smoke detection system that uses a common CCD camera sensor to detect smoke in images and trigger alarms. First, a proper background model is proposed to reliably extract smoke regions and avoid over-segmentation and false positives in outdoor scenarios where many distractors are present, such as moving trees or light reflexes. A novel Bayesian approach is adopted to detect smoke regions in the scene analyzing image energy by means of the Wavelet Transform coefficients and Color Information. A statistical model of image energy is built, using a temporal Gaussian Mixture, to analyze the energy decay that typically occurs when smoke covers the scene then the detection is strengthen evaluating the color blending between a reference smoke color and the input frame. The proposed system is capable of detecting rapidly smoke events both in night and in day conditions with a reduced number of false alarms hence is particularly suitable for monitoring large outdoor scenarios where common sensors would fail. An extensive experimental campaign both on recorded videos and live cameras evaluates the efficacy and efficiency of the system in many real world scenarios, such as outdoor storages and forests.

The information contained in digital video surveillance repositories can present relevant hints, when not even legal evidence, during investigations. As the amount of video data often forbids manual search, some tools have been developed during the past years in order to aid investigators in the look up process. We propose an application for forensic video analysis which aims at analysing the activities in a given scenario, particularly focusing on trajectories followed by people and their visual appearances. The recorded videos can be browsed by investigators thanks to a user-friendly interface, allowing easy information retrieval, through the choice of the best mining strategy. The underlying application architecture implements different feature and query models as well as query optimization strategies in order to return the best response in terms of both efficacy and efficiency.

This paper presents a method for comparing people trajectories for video surveillance applications, based on semi-directional statistics. In fact, the modelling of a trajectory as a sequence of angles, speeds and time lags, requires the use of a statistical tool capable to jointly consider periodic and linear variables. Our statistical method is compared with two state-of-the-art methods.