Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

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Elements Of Episodic Memory

A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

Visual navigation (vNavigation) is a key and fundamental technology for artificial agents' interaction with the environment to achieve advanced behaviors. Visual navigation for artificial agents with deep reinforcement learning (DRL) is a new research hotspot in artificial intelligence and robotics that incorporates the decision making of DRL into visual navigation. Visual navigation via DRL, an end-to-end method, directly receives the high-dimensional images and generates an optimal navigation policy. In this paper, we first present an overview on reinforcement learning (RL), deep learning (DL) and deep reinforcement learning (DRL). Then, we systematically describe five main categories of visual DRL navigation: direct DRL vNavigation, hierarchical DRL vNavigation, multi-task DRL vNavigation, memory-inference DRL vNavigation and vision-language DRL vNavigation. These visual DRL navigation algorithms are reviewed in detail. Finally, we discuss the challenges and some possible opportunities to visual DRL navigation for artificial agents.

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A Survey on Visual Navigation for Artificial Agents With Deep Reinforcement Learning

Novel method for localization via classification with reject option using omnidirectional images.Evaluation of feature extraction and machine learning techniques in omnidirectional images.Autonomous system based on mobile robot topological map localization.Validation in real and virtual environment.Novel virtual simulation environment and two data sets (virtual and real data). Mobile robot localization, which allows a robot to identify its position, is one of main challenges in the field of Robotics. In this work, we provide an evaluation of consolidated feature extractions and machine learning techniques from omnidirectional images focusing on topological map and localization tasks. The main contributions of this work are a novel method for localization via classification with reject option using omnidirectional images, as well as two novel omnidirectional image data sets. The localization system was analyzed in both virtual and real environments. Based on the experiments performed, the Minimal Learning Machine with Nearest Neighbors classifier and Local Binary Patterns feature extraction proved to be the best combination for mobile robot localization with accuracy of 96.7% and an Fscore of 96.6%.

A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images

Activity recognition which aims to accurately distinguish human actions in complex environments plays a key role in human-robot/computer interaction. However, long-lasting and similar actions will cause poor feature sequence extraction and thus lead to a reduction of the recognition accuracy. We propose a novel discriminative deep model (D3D-LSTM) based on 3D-CNN and LSTM for both single-target and interaction action recognition to improve the spatiotemporal processing performance. Our models have several notable properties: 1) A real-time feature fusion method is used to obtain a more representative feature sequence through composition of local mixtures for enhancing the performance of discriminating similar actions; 2) We introduce an improved attention mechanism that focuses on each frame individually by assigning different weights in real-time; 3) An alternating optimization strategy is proposed for our model to obtain parameters with the best performance. Because the proposed D3D-LSTM model is efficient enough to be used as a detector that recognizes various activities, a Real-set database is collected to evaluate action recognition in complex real-world scenarios. For long-term relations, we update the present memory state via the weight-controlled attention module that enables the memory cell to store better long-term features. The densely connected bimodal modal makes local perceptrons of 3D-Conv motion-aware and stores better short-term features. The proposed D3D-LSTM model has been evaluated through a series of experiments on the Real-set and open-source datasets, i.e. SBU-Kinect and MSR-action-3D. Experimental results show that the proposed D3D-LSTM model achieves new state-of-the-art results, including pushing the average rate of the SBU-Kinect to 92.40% and the average rate of the MSR-action-3D to 95.40%.

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A discriminative deep model with feature fusion and temporal attention for human action recognition

Graphical abstractDisplay Omitted HighlightsMBARAM enables topological map building with little or no human intervention.Does not require high-level cognitive and prior knowledge to function in a natural environment.Multiple sensory sources are processes simultaneously crucial for real-world robot navigation.Results show capability of generating topological map online used for localization. In this paper, a new network is proposed for automated recognition and classification of the environment information into regions, or nodes. Information is utilized in learning the topological map of an environment. The architecture is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) that comprises of two layers, input and memory. The input layer is formed using the Multiple Bayesian Adaptive Resonance Theory, which collects sensory data and incrementally clusters the obtained information into a set of nodes. In the memory layer, the clustered information is used as a topological map, where nodes are connected with edges. Nodes in the topological map represent regions of the environment and stores the robot location, while edges connect nodes and stores the robot orientation or direction. The proposed method, a Multi-channel Bayesian Adaptive Resonance Associative Memory (MBARAM) is validated using a number of benchmark datasets. Experimental results indicate that MBARAM is capable of generating topological map online and the map can be used for localization.

Multi-channel Bayesian Adaptive Resonance Associate Memory for on-line topological map building

In this paper we present an approach for on-line and incremental learning of human motion patterns through continuous observation of motion using novel Topological Gaussian Adaptive Resonance Hidden Markov Model (TGART-HMM). The observed human motion patterns are encoded in a novel modified version of Hidden Markov Model (HMM) called TGART-HMM. The on-line learning process consists of updating the structure of Hidden Markov Model using a topology-learning mechanism based on Gaussian Adaptive Resonance Theory (GART). The model size is adaptable based on the observed motion patterns. The resulting HMM structure is a graph where each node represents an encoded motion pattern. The parameters of TGART-HMM are updated incrementally to incorporate incessant motion patterns. The algorithm is tested on motion captured data to test the efficacy of the system.

Incremental on-line learning of human motion using Gaussian adaptive resonance hidden Markov model

In this paper, an unsupervised learning model of episodic memory is proposed. The proposed model, enhanced episodic memory adaptive resonance theory (EEM-ART), categorizes and encodes experiences of a robot to the environment and generates a cognitive map. EEM-ART consists of multilayer ART networks to extract novel events and encode spatio-temporal connection as episodes by incrementally generating cognitive neurons. The model connects episodes to construct a sensorimotor map for the robot to continuously perform path planning and goal navigation. Experimental results for a mobile robot indicate that EEM-ART can process multiple sensory sources for learning events and encoding episodes simultaneously. The model overcomes perceptual aliasing and robot localization by recalling the encoded episodes with a new anticipation function and generates sensorimotor map to connect episodes together to execute tasks continuously with little to no human intervention.

Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation

This paper presents a method with dynamic density topological structure generation for low-cost real-time vertical ladder detection from 3D point cloud data. Dynamic Density Growing Neural Gas (DD-GNG) is proposed to generate a dynamic density of the topological structure. The density of the structure and the number of nodes will be increased in the targeted object area. Feature extraction model is required to classify suspected objects for being processed in the next time process. After that, rungs of the vertical ladder is processed using an inlier-outlier method. Thus, the ladder detection model represents the ladder with a set of nodes and edges. Next, affordance detection is processed for detecting the feasible grasped location. To validate the effectiveness of the proposed method, a series of experiments are conducted on a 4-legged robot with a non-GPU board for real-time vertical ladder detection and climbing to validate the effectiveness of the proposed method. Results show that our proposed method able to detect and track the ladder structure in real-time with a much lower computational cost. The affordance of the ladder provides safety information for robot grasping.

Wei Hong Chin

Papers

A discriminative deep model with feature fusion and temporal attention for human action recognition

Multi-channel Bayesian Adaptive Resonance Associate Memory for on-line topological map building

Incremental on-line learning of human motion using Gaussian adaptive resonance hidden Markov model

Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation

Dynamic Density Topological Structure Generation for Real-Time Ladder Affordance Detection