16,402 research outputs found
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for
the automatic recognition of human actions in the video. Several attempts have
been made to capture frame-level salient aspects through attention but they
lack the capacity to effectively map the temporal relationships in between the
frames as they only capture a limited span of temporal dependencies. To this
end we propose a complete end-to-end supervised learning approach that can
better learn relationships between actions over time, thus improving the
overall segmentation performance. The proposed hierarchical recurrent attention
framework analyses the input video at multiple temporal scales, to form
embeddings at frame level and segment level, and perform fine-grained action
segmentation. This generates a simple, lightweight, yet extremely effective
architecture for segmenting continuous video streams and has multiple
application domains. We evaluate our system on multiple challenging public
benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech
Egocentric datasets, and achieves state-of-the-art performance. The evaluated
datasets encompass numerous video capture settings which are inclusive of
static overhead camera views and dynamic, ego-centric head-mounted camera
views, demonstrating the direct applicability of the proposed framework in a
variety of settings.Comment: Published in Pattern Recognition Letter
Building an Understanding of Human Activities in First Person Video using Fuzzy Inference
Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by interaction with objects. We leverage these object-centric activity definitions to develop a set of rules for a Fuzzy Inference System (FIS) that uses video features and the identification of objects to identify and classify activities. Further, we demonstrate that the use of FIS enhances the reliability of the system and provides enhanced explainability and interpretability of results over popular machine-learning classifiers due to the linguistic nature of fuzzy systems
SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal
How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze
Unsupervised segmentation of action segments in egocentric videos is a
desirable feature in tasks such as activity recognition and content-based video
retrieval. Reducing the search space into a finite set of action segments
facilitates a faster and less noisy matching. However, there exist a
substantial gap in machine understanding of natural temporal cuts during a
continuous human activity. This work reports on a novel gaze-based approach for
segmenting action segments in videos captured using an egocentric camera. Gaze
is used to locate the region-of-interest inside a frame. By tracking two simple
motion-based parameters inside successive regions-of-interest, we discover a
finite set of temporal cuts. We present several results using combinations (of
the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains
egocentric videos depicting several daily-living activities. The quality of the
temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image
Processing Application
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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