Publications by Simone Calderara

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.

Dangerous situations arise in everyday life and many efforts have been lavished to exploit technology to increase the level of safety in urban areas. Video analysis is absolutely one of the most important and emerging technology for security purposes. Automatic video surveillance systems commonly analyze the scene searching for moving objects. Well known techniques exist to cope with this problem that is commonly referred as change detection". Every time a dierence against a reference model is sensed, it should be analyzed to allow the system to discriminateamong a usual situation or a possible threat. When the sensor is a camera, motion is the key element to detect changes and moving objects must be correctly classied according to their nature. In this context we can distinguish among two dierent kinds of threat that can lead to dangerous situations in a video-surveilled environment. The first one is due to environmental changes such as rain, fog or smoke present in the scene. This kind of phenomena are sensed by the camera as moving pixelsand, subsequently as moving objects in the scene. This kind of threats shares some common characteristics such as texture, shape and color information and can be detected observing the features' evolution in time. The second situation arises whenpeople are directly responsible of the dangerous situation. In this case a subject is acting in an unusual way leading to an abnormal situation. From the sensor's point of view, moving pixels are still observed, but specic features and time-dependent statistical models should be adopted to learn and then correctly detect unusual and dangerous behaviors. With these premises, this chapter will present two different case studies. The rst one describes the detection of environmental changes in theobserved scene and details the problem of reliably detecting smoke in outdoor environments using both motion information and global image features, such as color information and texture energy computed by the means of the Wavelet transform.The second refers to the problem of detecting suspicious or abnormal people behaviors by means of people trajectory analysis in a multiple cameras video-surveillance scenario. Specically, a technique to infer and learn the concept of normality is proposed jointly with a suitable statistical tool to model and robustly compare people trajectories.

People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories mining that analyzes, in an integrated solution, several aspects of the trajectories such as location, shape and speed properties. Three different models are proposed to deal with non-idealities of the selected features in conjunction with a robust inexact- matching similarity measure for comparing sequences with different lengths. Experimental results in a real scenario demonstrates the efficacy of the framework in clustering people trajectories with the purpose of analyze frequent behaviors in complex environments.

This paper proposes a real-time system capable to extract andmodel object trajectories from a multi-camera setup with theaim of identifying abnormal paths. The trajectories are modeledas a sequence of positional distributions (2D Gaussians)and clustered in the training phase by exploiting an innovativedistance measure based on a global alignment techniqueand Bhattacharyya distance between Gaussians. An on-lineclassification procedure is proposed in order to on-the-fly classifynew trajectories into either “normal” or “abnormal” (in thesense of rarely seen before, thus unusual and potentially interesting).Experiments on a real scenario will be presented.

This paper proposes a system for people trajectory shape analysis by exploiting a statistical approach which accounts for sequences of both directional (the directions of the trajectory) and linear (the speeds) data. A semi-directional distribution (AWLG - Approximated Wrapped and Linear Gaussian) is used with a mixture to find main directions and speeds. A variational version of the mutual information criterion is proposed to prove the statistical dependency of the data. Then, in order to compare data sequences, we define an inexact method with a Kullback-Leibler-based distance measure and employ a global alignment technique is to handle sequences of different lengths and with local shifts or deformations. A comprehensive analysis of variable dependency and parameter estimation techniques are reported and evaluated on both synthetic and real data sets.