Adaptive visual attention based object recognition (original) (raw)
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Learning object recognition in a neurobotic system
2004
The robot has to identify and manipulate objects lying on this table. We evaluated the total object recognition performance and compared the effectiveness of different feature sets. The approach showed very encouraging results and meets real-time constraints.
VISUAL ATTENTION MODEL FOR MOBILE ROBOT NAVIGATION IN DOMESTIC ENVIRONMENT
Global Scientific Journals, 2020
Paying more attention to the elderly and disabled people has become an essential part of a developed society. It means they need to be looked after when the responsible people are not around. That means using an automated system is compulsory in ful filling this objective. The concept of visual attention model for mobile robot navigation in domestic environment is used in this re search paper. This solution is mainly aimed on elderly and disabled people. This helps them to obtain a desired object by action recognition using computer vision. In this paper it proposes a method to deliver an object that is required by the elderly or disabled user. First, the robot identifies the hand gesture shown by the user to identify the direction where the objects are used to complete this task. Then it identifies the hand gesture shown by the user, which is representing the required object number, by using the con vexity defects detection method. After that robot navigates to the target object by avoiding obstacles IR sensor is used to detect and avoid obstacles. In this process, objects are placed in predefined places in the room. Then the robot platform reaches the object shown by the user and then returns to the user. To measure the angles rotated by the platform, a gyro sensor is used. The main pro cess is calculating the distance values to the required objects. Kinect V2 camera is used to calculate the depth values.
Concurrent Object Identification and Localization for a Mobile Robot
Künstliche Intelligenz, 2000
Identification and localization of task-relevant objects is an essential problem for advanced service robots. We integrate state-of-the-art techniques both for object identification and object localization to solve this problem. Based on a multi- level spatial representation architecture, our approach inte- grates methods for mapping, self-localization and spatial rea- soning for navigation with visual attention, feature detection, and hierarchical neural classifiers.
Integrating Attention and Categorization Behaviors in Robotics
This work presents an active vision system for control tasks involving attention and pattern categorization based on visual sensory information. The system is currently implemented in a robot consisting of an articulated stereo-head with four degrees of freedom (pan, tilt, left and right verge). As a practical result, the robot is able to analyze all regions of its environment, selected according to salience maps. This involves performing attentional shifts and an efficient feature extraction and pattern recognition. Also the system incrementally construct an attentional map, and keep the map consistent with a current perception of the environment. Experimental result shows succesful applications in inspection, monitoring, or surveillance tasks.
Advances in Image and Video Processing, 2014
Assistive robotics technologies have been growing impact on at-home monitoring services to support daily life. One of the main research fields is to develop an autonomous mobile robot with the tasks detection, tracking, observation and analysis of the subject of interest in the indoor environment. The main challenges in such daily monitoring application, thus in visual search, are that the robot should track the subject successfully in several severe varying conditions. Recent color and depth image based visual search methods can help to handle part of the problems, such as changing illumination, occlusion, and etc. but these methods generally use large amount of training data by checking the whole scene with high redundancy to find the region of interest. Therefore, inspired by the idea that spatial memory can reveal novelty regions for finding the attention points as in Human Visual System (HVS), we proposed a simple and novel algorithm that integrates Kinect and Lidar(Light Detection And Ranging) sensor data to detect and track novelties using the environment map of the robot as a topdown approach without the necessity of large amount of training data. Then, novelty detection and tracking is achieved based on space based saliency map representing the novelty on the scene. Experimental results demonstrated that the proposed visual attention based scene analysis can handle various conditions stated and achieve high accuracy of novelty detection and tracking.
Offline Learning of Top-down Object based Attention Control
2008
Like humans and primates, artificial creatures like robots are limited in terms of allocation of their resources to huge sensory and perceptual information. Serial processing mechanisms are believed to have the major role on such limitation. Thus attention control is regarded as the same solution as humans in this regard but of course with different attention control mechanisms than those of parallel brain. In this paper, an algorithm is proposed for offline learning of top-down object based visual attention control by biasing the basic saliency based model of visual attention. Each feature channel and resolution of the basic saliency map is associated with a weight and a processing cost. Then a global optimization algorithm is used to find a set of parameters for detecting specific objects. Proposed method is evaluated over synthetic search arrays in pop-out and conjunction search tasks and also for traffic sign recognition on cluttered scenes.
Visual attention for robotic cognition: a survey
2011
The goal of the cognitive robotics research is to design robots with human-like cognition (albeit reduced complexity) in perception, reasoning, action planning, and decision making. Such a venture of cognitive robotics has developed robots with redundant number of sensors and actuators in order to perceive the world and act up on it in a human-like fashion. A major challenge to deal with these robots is managing the enormous amount of information continuously arriving through multiple sensors. The primates master this information management skill through their custom-built attention mechanism. Mimicking the attention behavior of the primates, therefore, has gained tremendous popularity in robotic research in the recent years (Bar-Cohen et al., Biologically Inspired Intelligent Robots, 2003, and B. Webb et al., Biorobotics, 2003). The difficulties of redundant information management, however, is the most severe in case of visual perception of the robots. Even a moderate size image of the natural scene generally contains enough visual information to easily overload the on-line decision making process of an autonomous robot. Modeling primates-like visual attention mechanism for the robot, therefore, is becoming more popular among the robotic researchers. A visual attention model enables the robot to selectively (and autonomously) choose a "behaviorally relevant" segment of visual information for further processing while relative exclusion of the others. This paper sheds light on the ongoing journey of robotics research to achieve a visual attention model which will serve as a component of cognition of the modern-day robots.
Robot Evolutionary Localization Based on Attentive Visual Short-Term Memory
Sensors, 2013
Cameras are one of the most relevant sensors in autonomous robots. However, two of their challenges are to extract useful information from captured images, and to manage the small field of view of regular cameras. This paper proposes implementing a dynamic visual memory to store the information gathered from a moving camera on board a robot, followed by an attention system to choose where to look with this mobile camera, and a visual localization algorithm that incorporates this visual memory. The visual memory is a collection of relevant task-oriented objects and 3D segments, and its scope is wider than the current camera field of view. The attention module takes into account the need to reobserve objects in the visual memory and the need to explore new areas. The visual memory is useful also in localization tasks, as it provides more information about robot surroundings than the current instantaneous image. This visual system is intended as underlying technology for service robot applications in real people's homes. Several experiments have been carried out, both with simulated and real Pioneer and Nao robots, to validate the system and each of its components in office scenarios.
Using Recognition to Guide a Robot's Attention
Robotics: Science and Systems IV, 2008
In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. This is demonstrated for object instances that have never been observed during training, and under partial occlusion and against cluttered backgrounds. Our approach builds on the Implicit Shape Model of Leibe and Schiele, and extends it to couple recognition to the provision of meta-data useful for a task. Meta-data can for example consist of part labels or depth estimates. We present experimental results on wheelchairs and cars.
Proceedings of the 2009 Ieee Rsj International Conference on Intelligent Robots and Systems, 2009
One of the key competencies required in modern robots is finding objects in complex environments. For the last decade, significant progress in computer vision and machine learning literatures has increased the recognition performance of well localized objects. However, the performance of these techniques is still far from human performance, especially in cluttered environments. We believe that the performance gap between robots and humans is due in part to humans' use of an attention system. According to cognitive psychology, the human visual system uses two stages of visual processing to interpret visual input. The first stage is a pre-attentive process perceiving scenes fast and coarsely to select potentially interesting regions. The second stage is a more complex process analyzing the regions hypothesized in the previous stage. These two stages play an important role in enabling efficient use of the limited cognitive resources available. Inspired by this biological fact, we propose a visual attentional object categorization approach for robots that enables object recognition in real environments under a critical time limitation. We quantitatively evaluate the performance for recognition of objects in highly cluttered scenes without significant loss of detection rates across several experimental settings.