11 research outputs found
Coarse Temporal Attention Network (CTA-Net) for Driver’s Activity Recognition
There is significant progress in recognizing traditional human activities
from videos focusing on highly distinctive actions involving discriminative
body movements, body-object and/or human-human interactions. Driver's
activities are different since they are executed by the same subject with
similar body parts movements, resulting in subtle changes. To address this, we
propose a novel framework by exploiting the spatiotemporal attention to model
the subtle changes. Our model is named Coarse Temporal Attention Network
(CTA-Net), in which coarse temporal branches are introduced in a trainable
glimpse network. The goal is to allow the glimpse to capture high-level
temporal relationships, such as 'during', 'before' and 'after' by focusing on a
specific part of a video. These branches also respect the topology of the
temporal dynamics in the video, ensuring that different branches learn
meaningful spatial and temporal changes. The model then uses an innovative
attention mechanism to generate high-level action specific contextual
information for activity recognition by exploring the hidden states of an LSTM.
The attention mechanism helps in learning to decide the importance of each
hidden state for the recognition task by weighing them when constructing the
representation of the video. Our approach is evaluated on four publicly
accessible datasets and significantly outperforms the state-of-the-art by a
considerable margin with only RGB video as input.Comment: Extended version of the accepted WACV 202
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
This paper presents a novel keypoints-based attention mechanism for visual
recognition in still images. Deep Convolutional Neural Networks (CNNs) for
recognizing images with distinctive classes have shown great success, but their
performance in discriminating fine-grained changes is not at the same level. We
address this by proposing an end-to-end CNN model, which learns meaningful
features linking fine-grained changes using our novel attention mechanism. It
captures the spatial structures in images by identifying semantic regions (SRs)
and their spatial distributions, and is proved to be the key to modelling
subtle changes in images. We automatically identify these SRs by grouping the
detected keypoints in a given image. The ``usefulness'' of these SRs for image
recognition is measured using our innovative attentional mechanism focusing on
parts of the image that are most relevant to a given task. This framework
applies to traditional and fine-grained image recognition tasks and does not
require manually annotated regions (e.g. bounding-box of body parts, objects,
etc.) for learning and prediction. Moreover, the proposed keypoints-driven
attention mechanism can be easily integrated into the existing CNN models. The
framework is evaluated on six diverse benchmark datasets. The model outperforms
the state-of-the-art approaches by a considerable margin using Distracted
Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions
(mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc:
6.30%) and Caltech-256 (Acc: 2.59%) datasets.Comment: Published in IEEE Transaction on Image Processing 2021, Vol. 30, pp.
3691 - 370
Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems
Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included
Federated broker system for pervasive context provisioning
Software systems that provide context-awareness related functions in pervasive computing environments are gaining momentum due to emerging applications, architectures and business models. In most context-aware systems, a central broker performs the functions of context acquisition, processing, reasoning and provisioning to facilitate context-consuming applications, but demonstrations of such prototypical systems are limited to small, focussed domains. In order to develop modern context-aware systems that are capable of accommodating emerging pervasive/ubiquitous computing scenarios, are easily manageable, administratively and geographically scalable, it is desirable to have multiple brokers in the system divided into administrative, network, geographic, contextual or load based domains. Context providers and consumers may be configured to interact only with their nearest, relevant or most convenient broker. This setup demands inter-broker federation so that providers and consumers attached to different brokers can interact seamlessly, but such a federation has not been proposed for context-aware systems. This article analyses the limiting factors in existing context-aware systems, postulates the design and functional requirements that modern context-aware systems need to accommodate, and presents a federated broker based architecture for provisioning of contextual information over large geographical and network spans