SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Advanced machine learning approaches for target detection, tracking and recognition

This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, learning and recognition. Novel solutions are proposed for each component of the ATR system based on several new advances in the field of computer vision and machine learning. Firstly, we introduce a simple and elegant feature, RelCom, and a boosted feature selection method to achieve a very low computational complexity target detector. Secondly, we present a particle filter based target tracking algorithm that uses a quad histogram based appearance model along with online feature selection. Further, we improve the tracking performance by means of online appearance learning where appearance learning is cast as an Adaptive Kalman filtering (AKF) problem which we formulate using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Then, we introduce an integrated tracking and recognition system that uses two generative models to accommodate the pose variations and maneuverability of different ground targets. Specifically, a tensor-based generative model is used for multi-view target representation that can synthesize unseen poses, and can be trained from a small set of signatures. In addition, a target-dependent kinematic model is invoked to characterize the target dynamics. Both generative models are integrated in a graphical framework for joint estimation of the target's kinematics, pose, and discrete valued identity. Finally, for target recognition we advocate the concept of a continuous identity manifold that captures both inter-class and intra-class shape variability among training targets. A hemispherical view manifold is used for modeling the view-dependent appearance. In addition to being able to deal with arbitrary view variations, this model can determine the target identity at both class and sub-class levels, for targets not present in the training data. The proposed components of the ATR system enable us to perform low computational complexity target detection with low false alarm rates, robust tracking of targets under challenging circumstances and recognition of target identities at both class and sub-class levels. Experiments on real and simulated data confirm the performance of the proposed components with promising results

Joint Target Tracking and Recognition Using Shape-based Generative Model

Recently a generative model that combines both of identity and view manifolds was proposed for multi-view shape modeling that was originally used for pose estimation and recognition of civilian vehicles from image sequences. In this thesis, we extend this model to both civilian and military vehicles, and examine its effectiveness for real-world automated target tracking and recognition (ATR) applications in both infrared and visible image sequences. A particle filter-based ATR algorithm is introduced where the generative model is used for shape interpolation along both the view and identity manifolds. The ATR algorithm is tested on the newly released SENSIAC (Military Sensing Information Analysis Center) infrared database along with some visible-band image sequences. Overall tracking and recognition performance is evaluated in terms of the accuracy of 3D position/pose estimation and target classification.</School of Electrical & Computer Engineerin

Object recognition in infrared imagery using appearance-based methods

Abstract unavailable please refer to PD