93,661 research outputs found
Visual surveillance by dynamic visual attention method
This paper describes a method for visual surveillance based on biologically motivated dynamic visual attention in video image sequences. Our system is based on the extraction and integration of local (pixels and spots) as well as global (objects) features. Our approach defines a method for the generation of an active attention focus on a dynamic scene for surveillance purposes. The system segments in accordance with a set of predefined features, including gray level, motion and shape features, giving raise to two classes of objects: vehicle and pedestrian. The solution proposed to the selective visual attention problem consists of decomposing the input images of an indefinite sequence of images into its moving objects, defining which of these elements are of the user\\s interest at a given moment, and keeping attention on those elements through time. Features extraction and integration are solved by incorporating mechanisms of charge and discharge?based on the permanency effect?, as well as mechanisms of lateral interaction. All these mechanisms have proved to be good enough to segment the scene into moving objects and background
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.Comment: Accepted to IEEE Transactions on Image Processin
Attention Mechanism for Adaptive Feature Modelling
This thesis presents groundbreaking contributions in machine learning by exploring and advancing attention mechanisms within deep learning frameworks. We introduce innovative models and techniques that significantly enhance feature recognition and analysis in two key application areas: computer vision recognition and time series modeling. Our primary contributions include the development of a dual attention mechanism for crowd counting and the integration of supervised and unsupervised learning techniques for semi-supervised learning. Furthermore, we propose a novel Dynamic Unary Convolution in Transformer (DUCT) model for generalized visual recognition tasks, and investigate the efficacy of attention mechanisms in human activity recognition using time series data from wearable sensors based on the semi-supervised setting.
The capacity of humans to selectively focus on specific elements within complex scenes has long inspired machine learning research. Attention mechanisms, which dynamically modify weights to emphasize different input elements, are central to replicating this human perceptual ability in deep learning. These mechanisms have proven crucial in achieving significant advancements across various tasks.
In this thesis, we first provide a comprehensive review of the existing literature on attention mechanisms. We then introduce a dual attention mechanism for crowd counting, which employs both second-order and first-order attention to enhance spatial information processing and feature distinction. Additionally, we explore the convergence of supervised and unsupervised learning, focusing on a novel semi-supervised method that synergizes labeled and unlabeled data through an attention-driven recurrent unit and dual loss functions. This method aims to refine crowd counting in practical transportation scenarios.
Moreover, our research extends to a hybrid attention model for broader visual recognition challenges. By merging convolutional and transformer layers, this model adeptly handles multi-level features, where the DUCT modules play a pivotal role. We rigorously evaluate DUCT's performance across critical computer vision tasks. Finally, recognizing the significance of time series data in domains like health surveillance, we apply our proposed attention mechanism to human activity recognition, analyzing correlations between various daily activities to enhance the adaptability of deep learning frameworks to temporal dynamics
Pirate stealth or inattentional blindness?:the effects of target relevance and sustained attention on security monitoring for experienced and naĂŻve operators
Closed Circuit Television (CCTV) operators are responsible for maintaining security in various applied settings. However, research has largely ignored human factors that may contribute to CCTV operator error. One important source of error is inattentional blindness--the failure to detect unexpected but clearly visible stimuli when attending to a scene. We compared inattentional blindness rates for experienced (84 infantry personnel) and naĂŻve (87 civilians) operators in a CCTV monitoring task. The task-relevance of the unexpected stimulus and the length of the monitoring period were manipulated between participants. Inattentional blindness rates were measured using typical post-event questionnaires, and participants' real-time descriptions of the monitored event. Based on the post-event measure, 66% of the participants failed to detect salient, ongoing stimuli appearing in the spatial field of their attentional focus. The unexpected task-irrelevant stimulus was significantly more likely to go undetected (79%) than the unexpected task-relevant stimulus (55%). Prior task experience did not inoculate operators against inattentional blindness effects. Participants' real-time descriptions revealed similar patterns, ruling out inattentional amnesia accounts
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
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
Person Search with Natural Language Description
Searching persons in large-scale image databases with the query of natural
language description has important applications in video surveillance. Existing
methods mainly focused on searching persons with image-based or attribute-based
queries, which have major limitations for a practical usage. In this paper, we
study the problem of person search with natural language description. Given the
textual description of a person, the algorithm of the person search is required
to rank all the samples in the person database then retrieve the most relevant
sample corresponding to the queried description. Since there is no person
dataset or benchmark with textual description available, we collect a
large-scale person description dataset with detailed natural language
annotations and person samples from various sources, termed as CUHK Person
Description Dataset (CUHK-PEDES). A wide range of possible models and baselines
have been evaluated and compared on the person search benchmark. An Recurrent
Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to
establish the state-of-the art performance on person search
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