476 research outputs found
Social media and successful retail operations in the hyper-customisation era
Full text linkIt's possible to increase both customer satisfaction and profitability, but careful planning is needed, write Usha Ramanathan, Nachiappan (Nachi) Subramanian and Guy Parrot
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[Blog] Social media and successful retail operations in the hyper-customisation era
No full textIt's possible to increase both customer satisfaction and profitability, but careful planning is needed, write Usha Ramanathan, Nachiappan (Nachi) Subramanian and Guy Parrot
[Blog] Social media and successful retail operations in the hyper-customisation era
No full textIt's possible to increase both customer satisfaction and profitability, but careful planning is needed, write Usha Ramanathan, Nachiappan (Nachi) Subramanian and Guy Parrot
Multitask Linear Discriminant Analysis for View Invariant Action Recognition
No full textRobust action recognition under viewpoint changes has received considerable attention recently. To this end, self-similarity matrices (SSMs) have been found to be effective view-invariant action descriptors. To enhance the performance of SSM-based methods, we propose multitask linear discriminant analysis (LDA), a novel multitask learning framework for multiview action recognition that allows for the sharing of discriminative SSM features among different views (i.e., tasks). Inspired by the mathematical connection between multivariate linear regression and LDA, we model multitask multiclass LDA as a single optimization problem by choosing an appropriate class indicator matrix. In particular, we propose two variants of graph-guided multitask LDA: 1) where the graph weights specifying view dependencies are fixed a priori and 2) where graph weights are flexibly learnt from the training data. We evaluate the proposed methods extensively on multiview RGB and RGBD video data sets, and experimental results confirm that the proposed approaches compare favorably with the state-of-the-art.</p
A Multi-task Learning Framework for Head Pose Estimation under Target Motion
Full text linkRecently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings
Jointly Estimating Interactions and Head, Body Pose of Interactors from Distant Social Scenes
No full textWe present joint estimation of F-formations and head, body pose of interactors in a social scene captured by surveillance cameras. Unlike prior works that have focused on (a) discov-ering F-formations based on head pose and position cues, or (b) jointly learned head and body pose of individuals based on anatomic constraints, we exploit positional and pose cues characterizing interactors and interactions to jointly infer both (a) and (b). We show how the joint inference frame-work benefits both F-formation and head, body pose esti-mation accuracy via experiments on two social datasets
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses
No full textIn this work, we present DECAF-a multimodal data set for decoding user physiological responses to affective multimedia content. Different from data sets such as DEAP [15] and MAHNOB-HCI [31], DECAF contains (1) brain signals acquired using the Magnetoencephalogram (MEG) sensor, which requires little physical contact with the user's scalp and consequently facilitates naturalistic affective response, and (2) explicit and implicit emotional responses of 30 participants to 40 one-minute music video segments used in [15] and 36 movie clips, thereby enabling comparisons between the EEG versus MEG modalities as well as movie versus music stimuli for affect recognition. In addition to MEG data, DECAF comprises synchronously recorded near-infra-red (NIR) facial videos, horizontal Electrooculogram (hEOG), Electrocardiogram (ECG), and trapezius-Electromyogram (tEMG) peripheral physiological responses. To demonstrate DECAF's utility, we present (i) a detailed analysis of the correlations between participants' self-assessments and their physiological responses and (ii) single-trial classification results for valence, arousal and dominance, with performance evaluation against existing data sets. DECAF also contains time-continuous emotion annotations for movie clips from seven users, which we use to demonstrate dynamic emotion prediction
Connecting meeting behavior with Extraversion - A systematic study
No full textThis work investigates the suitability of medium-grained meeting behaviors, namely, speaking time and social attention, for automatic classification of the Extraversion personality trait. Experimental results confirm that these behaviors are indeed effective for the automatic detection of Extraversion. The main findings of our study are that: 1) Speaking time and (some forms of) social gaze are effective indicators of Extraversion, 2) classification accuracy is affected by the amount of time for which meeting behavior is observed, 3) independently considering only the attention received by the target from peers is insufficient, and 4) distribution of social attention of peers plays a crucial role.</p
Joint Estimation of Human Pose and Conversational Groups from Social Scenes
No full textDespite many attempts in the last few years, automatic analysis of social scenes captured by wide-angle camera networks remains a very challenging task due to the low resolution of targets, background clutter and frequent and persistent occlusions. In this paper, we present a novel framework for jointly estimating (i) head, body orientations of targets and (ii) conversational groups called F-formations from social scenes. In contrast to prior works that have (a) exploited the limited range of head and body orientations to jointly learn both, or (b) employed the mutual head (but not body) pose of interactors for deducing F-formations, we propose a weakly-supervised learning algorithm for joint inference. Our algorithm employs body pose as the primary cue for F-formation estimation, and an alternating optimization strategy is proposed to iteratively refine F-formation and pose estimates. We demonstrate the increased efficacy of joint inference over the state-of-the-art via extensive experiments on three social datasets
Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-formations from Surveillance Videos
No full textWe present a novel approach for jointly estimating targets' head, body orientations and conversational groups called F-formations from a distant social scene (e.g., a cocktail party captured by surveillance cameras). Differing from related works that have (i) coupled head and body pose learning by exploiting the limited range of orientations that the two can jointly take, or (ii) determined F-formations based on the mutual head (but not body) orientations of interactors, we present a unified framework to jointly infer both (i) and (ii). Apart from exploiting spatial and orientation relationships, we also integrate cues pertaining to temporal consistency and occlusions, which are beneficial while handling low-resolution data under surveillance settings. Efficacy of the joint inference framework reflects via increased head, body pose and F-formation estimation accuracy over the state-of-the-art, as confirmed by extensive experiments on two social datasets
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