Search

Intelligent systems, like driver assistance systems, remain within vehicles and help drivers by providing essential information about traffic, blockage of roads, and possible routes for safe driving. The objective of scene text spotting in a driver assistance system is to localize and recognize scene texts, signs of milestones, traffic panels, and road marks in natural scene images. However, text edges get fainted due to adverse weather conditions, like fog, rain, smog, or poor contrast. This makes the task of spotting more challenging. In this paper, we propose an end-to-end trainable deep neural network, known as Met-MLTS, that can address the issue of spotting multi-oriented text instances in scene images captured in adverse meteorological conditions. It localizes words, predicts script class, and performs word spotting for every rotated bounding box. It is a fast multilingual scene text spotter that utilizes hierarchical spatial context, channel-wise inter-dependencies, and semantic edge supervision to localize and recognize words and predict script class in scene images using smartphones. We explore inter-class interference to reduce the misclassification problem. A light-weight recognition module for multilingual character segmentation, word-level recognition, and script identification is incorporated. We demonstrate the efficacy of our spotting network on resource-constraint devices. [ABSTRACT FROM AUTHOR]

The ability to recognize human actions in a video is challenging due to the complex nature of video data and the subtlety of human actions. Human activities often get associated with surrounding objects and occur in specific scene contexts. Existing action recognition systems are incapable of separating human actions from representation biases, like co-occurring objects and underlying scene, which often dominate subtle human actions. In this paper, we address the issue of factorization of human actions into the activity performed by the actor, co-occurring objects, and underlying context to mitigate the influence of representation biases when they are irrelevant to the action in consideration. We propose a deep neural network architecture, denoted by FactorNet, for efficient action recognition in videos with long temporal duration. We design an attention mechanism that separates an actor from the associated objects and co-occurring scene followed by capturing long-range temporal context. We perform a comprehensive set of experimentation on six benchmark datasets to show the efficacy of our architecture. To train a model using recent video-based action datasets certainly capture and leverages such bias. The supervised representation may not be competent to new action classes. We therefore design a new dataset, known as FactNet, which consists of activity-object-scene related actions that occur in day-to-day applications. Dataset Link: FactNet. [ABSTRACT FROM AUTHOR]