Independent Motion Detection Research Articles

Enhanced multifunctional sensing in human motion with speed-controlled coaxial wet-spun hollow MWCNT-TPU/TPU smart fibers

In the field of personal health management and athletic performance optimization, the demand for multi-parameter human signal monitoring devices is rapidly increasing. Currently, flexible sensors produced through wet spinning techniques face challenges of simplistic structure and functionality, as well as difficulty in balancing strength and sensitivity. In-depth research is needed to develop high-performance wet spinning techniques, which will lay the foundation for commercial smart garments capable of unobtrusively monitoring human motion in the future. This study innovatively employs specialized slurry formulations and spinning speed controls to utilize the timing differences in fiber precipitation and the affinity among similar materials. Through the coaxial wet spinning process, it enables the continuous, large-scale manufacturing of hollow core–shell fibers, MT/T-S5, which feature a dense inner layer and a porous outer conductive layer. This special structure endows the fibers with high strength, high signal stability, and capabilities for both active and passive heating. These fibers achieve a maximum gauge factor (GF) 65.2 in stretch sensing applications and can detect pressures as low as 0.1 N. The MT/T-S5 smart fibers can sense human joint movements, and through connection with an Arduino microcontroller, they can control a robotic hand and perform independent and real-time motion detection of five fingers to distinguish gestures. This research not only provides a new type of fiber material for the development of future smart textiles but also reveals a formation mechanism for hollow core–shell fibers in the field of wet spinning. This discovery can offer valuable inspiration for fiber structural design in future research.

Medical image registration with object deviation estimation through motion vectors using octave and level sampling

Medical image analysis presents a significant problem in the field of image registration. Recently, medical image registration has been recognized as a helpful tool for medical professionals. Current state-of-the-art approaches solely focus on source image registration and lack quantitative measurement for object deviation in terms of loosening, subsidence and anteversion related to surgery. In this article, we have provided motion vectors for recognizing the object deviation, in addition to detecting and selecting the feature points. Firstly, the feature points will be detected using Hessian matrix determinants and octave and level sampling. Then the strongest feature points are selected which will be utilized for identifying the object deviation with respect to the reference image through motion vectors. The objective of this work is to combine image registration and temporal differencing to achieve independent motion detection. In comparison to state-of-the-art approaches, the proposed methodology achieves higher Information Ratio (IR), Mutual Information Ratio (MIR) and their lower bounds for image registration.

Joint detection and tracking of independently moving objects in stereo sequences using scale-invariant feature transform features and particle filter

A scale-invariant feature transform (SIFT)-based particle filter algorithm is presented for joint detection and tracking of independently moving objects in stereo sequences observed by uncalibrated moving cameras. The major steps include feature detection and matching, moving object detection based on multiview geometric constraints, and tracking based on particle filter. Our contributions are first, a novel closed-loop mapping (CLM) multiview matching scheme proposed for stereo matching and motion tracking. CLM outperforms several state-of-the-art SIFT matching methods in terms of density and reliability of feature correspondences. Our second contribution is a multiview epipolar constraint derived from the relative camera positions in pairs of consecutive stereo views for independent motion detection. The multiview epipolar constraint is able to detect moving objects followed by moving cameras in the same direction, a configuration where the epipolar constraint fails. Our third contribution is a proposed dimensional variable particle filter for joint detection and tracking of independently moving objects. Multiple moving objects entering or leaving the field of view are handled effectively within the proposed framework. Experimental results on real-world stereo sequences demonstrate the effectiveness and robustness of our method.

Linear two-view multiple plane geometry

In this contribution we present a method for direct linear multiple plane estimation and optimization. The application is the detection of independent motion by background subtraction or temporal differences using image stabilization. Given a set of consistent homographies and a corresponding image segmentation for an image pair, it is possible to improve the image stabilization by locally warping. We show that linear constraints can be induced to homography estimation to ensure consistency. In this context consistency means that the homographies correspond to the same relative orientation but to different 3D planes. The relative orientation can be derived by measurements of additional sensors, e.g., inertial navigation system, odometry, or can be computed from the fundamental or essential matrix. The method is compared to standard estimation of homographies with simulated data, and the capability of the method is shown using depth estimation on the Middlebury-Stereo Benchmark dataset.

A Holistic, In-Compression Approach to Video Segmentation for Independent Motion Detection

This paper develops a large scale surveillance video data unsupervised segmentation technique regarding whether there is a presence of independent motion. We propose a holistic, in-compression approach to efficient video segmentation. By efficient, we mean that the processing speed is close to or even faster than real-time in normal platforms (we do not assume using special hardware or any parallel machines) while still maintaining a good quality segmentation. Theoretical and experimental analyses demonstrate and validate the holistic, in-compression approach to solving for video segmentation problem for independent motion detection.

Dual-task slowing and the effects of cross-task compatibility.

Recent studies have shown that visual encoding requires central processing, evidenced in the form of dual-task interference on response processes of a reaction time (RT) task. We report two experiments examining such dual-task interference: (1) at global level, to establish that visual encoding indeed requires central processing; and (2) at code level, to see whether two tasks may share the same representational codes. To this end, we manipulated the compatibility relation between the response codes for an auditory choice RTtask and the stimulus codes for a logically independent visual motion detection task. The results showed significant dual-task slowing of the response for the auditory task, demonstrating the interference at a global level. The effects of cross-task compatibility (CTC) were obtained at the short stimulus onset asynchrony (SOA), showing that the RTs were faster for compatible trials. Furthermore, the CTC effect remained even when the visual stimulus did not need to be "consolidated" (Jolicoeur & Dell'Acqua, 1998) for reporting, while the response postponement effect was greatly reduced. We interpret these results as indicating that the present two tasks share both "central" processes and common representational codes, but that these two levels of dual-task interference can be dissociated.

Independent motion detection in 3D scenes

This paper presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: 1) two/multiview camera motion constraint (for instance, the epipolar/trilinear constraint) and 2) shape constancy constraint. Previous approaches to the problem either use only partial constraints, or rely on dense correspondences or flow. We employ both the fundamental constraints in an algorithm that does not demand a priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated that for a class of scenes, called sparse 3D scenes in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction, and verification of the fundamental constraints. Results of the algorithm on some difficult sparse 3D scenes are promising.

Detection of Independent Motion Using Directional Motion Estimation

It is shown that the problem of independent motion detection can be addressed by analyzing constraints on low-dimensional directional (projected) components of flow fields. We construct a robust algorithm, implemented as a recursive filter, to extract directional motion parameters from long image sequences. Based on this, a qualitative approach is described to detect independent motion, involving a combination of robust line-fitting and one-dimensional search. The low-dimensional projections onto subspaces facilitate efficient dynamic self-adaptation of detection thresholds to achieve good performance under changing operational conditions. The analysis is extended to long image sequences by incorporating tracking and spatio-temporal filtering. The approach is applicable to general camera motion and cluttered scenes using a wide range of camera fields of view. We demonstrate it on a variety of real image sequences.

Robust Regression for the Detection of Independent 3D Motion by a Binocular Observer

A method is proposed for the visual detection of objects that move independently of the observer in a 3D dynamic environment. Many of the existing techniques for solving this problem are based on 2D motion models, which is equivalent to assuming that all the objects in a scene are at a constant depth from the observer. Although such methods perform well if this assumption holds, they may give erroneous results when applied to scenes with large depth variations. Additionally, many of the existing techniques rely on the computation of optical flow, which amounts to solving the ill-posed correspondence problem. In this paper, independent 3D motion detection is formulated using 3D models and is approached as a problem of robust regression applied to visual input acquired by a binocular, rigidly moving observer. Similar analysis is applied both to the stereoscopic data taken by a non-calibrated stereoscopic system and to the motion data obtained from successive frames in time. Least Median of Squares (LMedS) estimation is applied to stereoscopic data to produce maps of image regions characterized by a dominant depth. LMedS is also applied to the motion data that are related to the points at the dominant depth, to segment the latter with respect to 3D motion. In contrast to the methods that rely on 2D models, the proposed method performs accurately, even in the case of scenes with large depth variations. Both stereo and motion processing is based on the normal flow field, which (in contrast to the optical flow field) can be robustly computed from the spatiotemporal derivatives of the image intensity function. Although parts of the proposed scheme have non-trivial computational requirements, computations can be expedited by various ways which are discussed in detail. This is also demonstrated by an on-board implementation of the method on a mobile robotic platform. The method has been evaluated using synthetic as well as real data. Sample results show the effectiveness and robustness of the proposed scheme.

Detection and tracking of independent motion

We describe an efficient method of using a translating camera to detect and track independently translating objects and assess the likelihood of a collision. By analysing the underlying geometry, it is shown that the tracking is reduced to two independent linear searches for a single feature in the image plane. Results are presented for both an off-line and a real time implementation using no special hardware. The method is completely automatic and shown to be accurate and robust.

Infants' sensitivity to uniform motion

Uniform motion across the retina is a powerful cue to the perception of self-motion. In spite of its importance for adaptive functioning, little is known about the early development of uniform motion sensitivity. Six-, 12-, and 18-week-old infants viewed random-dot kinematograms depicting leftward or rightward uniform motion. The display induced optokinetic nystagmus (OKN), which a trained observer used to judge the direction of target motion. Both speed of motion and directional coherence were varied to obtain independent motion detection thresholds. Infants of all three ages could detect uniform motion, and their detection thresholds were constant during this period of development. This is in contrast to the clear improvements in relative motion sensitivity noted previously between 6 and 18 weeks of age with a preferential looking (PL) paradigm. The developmental differences between these studies may result from: (1) separate mechanisms for detecting uniform (absolute) and differential (relative) motion; or (2) separate mechanisms underlying OKN and PL response measures.

Closing the loop: detection and pursuit of a moving object by a moving observer

We present an integrated system which is able to pursue a moving object and maintain the object centered in the image by controlling a robot-head. The system contains three parts: independent motion detection, tracking, and control of a robot-head. This paper focuses on the detection mechanism, and briefly discusses the tracking and control issues. The system runs continuously in time and updates the object localization at a frame-rate of 25 Hz. The moving object can be tracked, although the observer performs an unknown independent motion, involving both translation and rotation. We focus on a simple motion detection algorithm, since computational cost is of major importance for real-time systems with feedback. The algorithm is noniterative and computationally inexpensive. The running time complexity is O ( n) for an image containing n pixels. The image-processing takes place on a MaxVideo 200 pipeline processor, and the head control algorithm is running on a Transputer T800 network. Some offline experimental results are presented, where comparisons are made between affine and translational image motion models.

Early detection of independent motion from active control of normal image flow patterns

An important initial step in interpreting a dynamic scene is to detect moving objects in the environment. This paper presents a novel solution to the problem of early motion detection by a moving observer. The solution requires the observer to be active in the acquisition of images thereby controlling the optical flow pattern due to egomotion. A theoretical analysis is done based on geometric considerations to establish conditions that are necessary and sufficient to guarantee motion detection at a point. The detection problem is posed in terms of locally computable image quantities (the normal image flow) which this makes it implementable in real time. The performance of the technique can be improved by imposing any applicable constraint; this is demonstrated for the detection of the motions of "compact" objects satisfying a size bound. The goal is to design a flexible and efficient early motion detection strategy that can be tailored to the needs of a particular navigation system.