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Pedestrian detection is considered as an active area of research and the advent of autonomous vehicles for a smarter mobility has spearheaded the research in this field. In this paper, design of a real-time pedestrian detection system for autonomous vehicles is proposed and its performance is evaluated using images from standard datasets as well as realtime video input. The proposed system is designed...
ORB-SLAM is a feature-based simultaneous localization and mapping (SLAM) system. It has achieved good results in tracking, mapping and loop closing. However, the map created by ORB-SLAM with the monocular camera can not get the real scale. This paper presents an improving ORB SLAM system that helps to alleviate this issue by defining a baseline initialization procedure. We take two relative poses...
Robots are typically equipped with multiple complementary sensors such as cameras and laser range finders. Camera generally provides dense 2D information while range sensors give sparse and accurate depth information in the form of a set of 3D points. In order to represent the different data sources in a common coordinate system, extrinsic calibration is needed. This paper presents a pipeline for...
The feature matching and tracking of binocular vision inter-frame images have been divided into three parts in this paper. Before image feature extraction, an image binarization segmentation method based on HSV color space is used to extract the target in order to reduce the searching range of feature points and improve the matching efficiency. About feature matching, a modified ORB method combining...
Attacks on authentication services are major security concerns. Password-based authentication systems can be compromised using known techniques, such as brute force and dictionary-based attacks. Biometric-based authentication systems are becoming the preferred choice to replace password-based authentication systems. Among several variations of biometrics (e.g., face, eye, fingerprint), iris-based...
Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection...
Biometrics is a growing research field due to an increasing demand for safety and security in both the public and private. Biometric recognition refers to the identification of a person by analyzing its physiological and/or behavioral traits. Among several physiological traits, palmprint has become an attractive characteristic due to its reliability, flexibility, stability, non-intrusiveness and discriminating...
In recent years, video surveillance technology has become ubiquitous in every sphere of our life. But automated video surveillance generates huge quantities of data, which ultimately does rely upon manual inspection at some stage. The present work aims to address this ever increasing gap between the volumes of actual data generated and the volume that can be reasonably inspected manually. It is laborious...
Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images. However, in most cases image-sequences, rather only single images, are readily available. To this extent, none of the proposed learning-based approaches exploit the valuable constraint of temporal smoothness, often leading...
Row-wise exposure delay present in CMOS cameras is responsible for skew and curvature distortions known as the rolling shutter (RS) effect while imaging under camera motion. Existing RS correction methods resort to using multiple images or tailor scene-specific correction schemes. We propose a convolutional neural network (CNN) architecture that automatically learns essential scene features from a...
Person re-identification (Re-ID) remains a challenging problem due to significant appearance changes caused by variations in view angle, background clutter, illumination condition and mutual occlusion. To address these issues, conventional methods usually focus on proposing robust feature representation or learning metric transformation based on pairwise similarity, using Fisher-type criterion. The...
Scene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition, especially in the context of convolutional neural networks (ConvNets), has not been previously studied. In this paper, we propose the extraction and use of scene flow for action recognition from RGB-D data. Previous works...
In the process of aircraft residual ice detection based on near infrared spectrum method, the qualities of the images, such as brightness and contrast, are easily influenced by sunlight, weather and other factors. Therefore, it is hard to precisely match the feature points. In this paper, an improved Harris local invariant feature matching method is proposed. The improved Harris feature point detection...
In this paper, we propose an image registration algorithm based on improved SIFT (Scale-Invariant Feature Transform) algorithm and essential matrix estimation based on RANSAC (Random Sample Consensus) and AC-RANSAC (A Contrario RANSAC) algorithm. So that in the 3D reconstruction, we can directly restore the parameters of the camera by using the essential matrix model estimated by image registration...
The tremendous growth in the availability of texture+depth(RGBD) content has motivated researchers to focus into the area of features that uses geometrical information given by depth. These features are more efficient and robust than the existing 2D features under large variations in camera viewing angles. In this paper, the performance evaluation of the keypoint detector for two views of a scene...
We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS). The biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a...
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments...
First-person videos have unique characteristics such as heavy egocentric motion, strong preceding events, salient transitional activities and post-event impacts. Action recognition methods designed for third person videos may not optimally represent actions captured by first-person videos. We propose a method to represent the high level dynamics of sub-events in first-person videos by dynamically...
Re-identification of people in surveillance footage must cope with drastic variations in color, background, viewing angle and a persons pose. Supervised techniques are often the most effective, but require extensive annotation which is infeasible for large camera networks. Unlike previous supervised learning approaches that require hundreds of annotated subjects, we learn a metric using a novel one-shot...
Due to variations in pose, angle and illumination condition, a person's appearance is significantly different in two different views, which makes person re-identification(re-id) intrinsically difficult. In this paper, we propose a person re-id method which learns Convolutional Neural Networks (CNNs) feature representations from joint-dataset learning. The CNN features extracted from all levels of...
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