Powered lower limb prostheses have potential to improve the quality of life of individuals with amputations by enabling all daily activities. However, seamless ambulation mode recognition is necessary to achieve this goal and is not yet a clinical reality. Current intent recognition systems use mechanical and EMG sensors to estimate prosthesis and user status. We propose to complement these systems by integrating information about the environment obtained through the depth sensing. This paper presents the design, characterization, and the early validation of a novel stair segmentation system based on Microsoft Kinect. Static and dynamic tests were performed. A first experiment showed how the resolution of the depth camera affects the speed and the accuracy of segmentation. A second test proved the robustness of the algorithm to different staircases. Finally, we performed an online walking test with the stair segmentation and related measures recorded online at >5 frames/s. Experimental results show that the proposed algorithm allows for an accurate estimate of distance, angle of intersection, number of steps, stair height, and stair depth for a set of stairs in the environment. The online test produced an estimate of whether the individual was approaching stairs in real time with approximately 98.8% accuracy.

Depth Sensing for Improved Control of Lower Limb Prostheses

Traffic sign recognition system (TSRS) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signs accurately and effectively can improve the driving safety. This paper brings forward a traffic sign recognition technique on the strength of deep learning, which mainly aims at the detection and classification of circular signs. Firstly, an image is preprocessed to highlight important information. Secondly, Hough Transform is used for detecting and locating areas. Finally, the detected road traffic signs are classified based on deep learning. In this article, a traffic sign detection and identification method on account of the image processing is proposed, which is combined with convolutional neural network (CNN) to sort traffic signs. On account of its high recognition rate, CNN can be used to realize various computer vision tasks. TensorFlow is used to implement CNN. In the German data sets, we are able to identify the circular symbol with more than 98.2% accuracy.

Traffic Sign Detection and Recognition Based on Convolutional Neural Network

Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.

Traffic sign detection and classification using colour feature and neural network

The traffic sign detection and recognition is an integral part of Advanced Driver Assistance System (ADAS). Traffic signs provide information about the traffic rules, road conditions and route directions and assist the drivers for better and safe driving. Traffic sign detection and recognition system has two main stages: The first stage involves the traffic sign localization and the second stage classifies the detected traffic signs into a particular class. This article provides the challenges involved in the real-time detection of traffic signs. A review of the existing detection methods such as color based, shape based and learning based detection is presented. It also discusses about the feature matching and machine learning algorithms used in the traffic sign recognition stage.

Automatic traffic sign detection and recognition: A review

Traffic object detection and recognition systems play an essential role in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV). In this research, we focus on four important classes of traffic objects: traffic signs, road vehicles, pedestrians, and traffic lights. We first review the major traditional machine learning and deep learning methods that have been used in the literature to detect and recognize these objects. We provide a vision-based framework that detects and recognizes traffic objects inside and outside the attentional visual area of drivers. This approach uses the driver 3D absolute coordinates of the gaze point obtained by the combined, cross-calibrated use of a front-view stereo imaging system and a non-contact 3D gaze tracker. A combination of multi-scale HOG-SVM and Faster R-CNN-based models are utilized in the detection stage. The recognition stage is performed with a ResNet-101 network to verify sets of generated hypotheses. We applied our approach on real data collected during drives in an urban environment with the RoadLAB instrumented vehicle. Our framework achieved 91% of correct object detections and provided promising results in the object recognition stage. 

Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers

Traffic sign detection and recognition plays an important role in driver assistance system especially in complexity environment. Firstly, RGB image is converted to standardization image only contained 8 colors for reducing computational burden. Only interesting color components are extracted as candidate region for further recognition. Then HOG descriptor is considered as detection characteristic of candidate regions. Finally, Zernike moment is combined with support vector machine for traffic sign recognition. Experiments show that it could defense of shadow, rainy days, and dim by take advantage of color standardization and Zernike moments.

Traffic sign detection and recognition using color standardization and Zernike moments

A simultaneous localization and mapping (SLAM) approach based on RGB-D sensor in indoor environment is presented in this paper. The S-NARF feature of point cloud was extracted, and then used for point cloud registration. In particular, a semi-random search strategy is proposed for the loop closures detection. The poses estimation was optimized under the g2o framework. The experiments demonstrate that the approach can effectively solve SLAM problem in indoor environment and the poses estimation have met the demand for precision.

RGB-D sensor based mobile robot SLAM in indoor environment

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Traffic sign detection and recognition using color standardization and Zernike moments

RGB-D sensor based mobile robot SLAM in indoor environment