Aging Impairs Audiovisual Facilitation of Object Motion Within Self-Motion

2018 ◽  
Vol 31 (3-4) ◽  
pp. 251-272 ◽  
Author(s):  
Eugenie Roudaia ◽  
Finnegan J. Calabro ◽  
Lucia M. Vaina ◽  
Fiona N. Newell
Keyword(s):  
Older Adults ◽  
Target Detection ◽  
Moving Objects ◽  
Broadband Noise ◽  
Object Motion ◽  
Detection Accuracy ◽  
Visual Condition ◽  
Speed Level ◽  
Self Motion ◽  
Independent Motion

The presence of a moving sound has been shown to facilitate the detection of an independently moving visual target embedded among an array of identical moving objects simulating forward self-motion (Calabro et al., Proc. R. Soc. B, 2011). Given that the perception of object motion within self-motion declines with aging, we investigated whether older adults can also benefit from the presence of a congruent dynamic sound when detecting object motion within self-motion. Visual stimuli consisted of nine identical spheres randomly distributed inside a virtual rectangular prism. For 1 s, all the spheres expanded outward simulating forward observer translation at a constant speed. One of the spheres (the target) had independent motion either approaching or moving away from the observer at three different speeds. In the visual condition, stimuli contained no sound. In the audiovisual condition, the visual stimulus was accompanied by a broadband noise sound co-localized with the target, whose loudness increased or decreased congruent with the target’s direction. Participants reported which of the spheres had independent motion. Younger participants showed higher target detection accuracy in the audiovisual compared to the visual condition at the slowest speed level. Older participants showed overall poorer target detection accuracy than the younger participants, but the presence of the sound had no effect on older participants’ target detection accuracy at either speed level. These results indicate that aging may impair cross-modal integration in some contexts. Potential reasons for the absence of auditory facilitation in older adults are discussed.

scholarly journals Causal inference accounts for heading perception in the presence of object motion

2019 ◽  
Vol 116 (18) ◽  
pp. 9060-9065 ◽  
Author(s):  
Kalpana Dokka ◽  
Hyeshin Park ◽  
Michael Jansen ◽  
Gregory C. DeAngelis ◽  
Dora E. Angelaki
Keyword(s):  
Causal Inference ◽  
Virtual World ◽  
Moving Objects ◽  
Object Motion ◽  
Image Motion ◽  
Inference Problem ◽  
Accuracy And Precision ◽  
The World ◽  
Self Motion ◽  
The Brain

The brain infers our spatial orientation and properties of the world from ambiguous and noisy sensory cues. Judging self-motion (heading) in the presence of independently moving objects poses a challenging inference problem because the image motion of an object could be attributed to movement of the object, self-motion, or some combination of the two. We test whether perception of heading and object motion follows predictions of a normative causal inference framework. In a dual-report task, subjects indicated whether an object appeared stationary or moving in the virtual world, while simultaneously judging their heading. Consistent with causal inference predictions, the proportion of object stationarity reports, as well as the accuracy and precision of heading judgments, depended on the speed of object motion. Critically, biases in perceived heading declined when the object was perceived to be moving in the world. Our findings suggest that the brain interprets object motion and self-motion using a causal inference framework.

scholarly journals Flow parsing and biological motion

2021 ◽  
Author(s):  
Katja M. Mayer ◽  
Hugh Riddell ◽  
Markus Lappe
Keyword(s):  
Prior Knowledge ◽  
Relative Motion ◽  
Biological Motion ◽  
Moving Objects ◽  
Human Motion ◽  
Object Motion ◽  
Biological Features ◽  
Typical Object ◽  
Point Light ◽  
Self Motion

AbstractFlow parsing is a way to estimate the direction of scene-relative motion of independently moving objects during self-motion of the observer. So far, this has been tested for simple geometric shapes such as dots or bars. Whether further cues such as prior knowledge about typical directions of an object’s movement, e.g., typical human motion, are considered in the estimations is currently unclear. Here, we adjudicated between the theory that the direction of scene-relative motion of humans is estimated exclusively by flow parsing, just like for simple geometric objects, and the theory that prior knowledge about biological motion affects estimation of perceived direction of scene-relative motion of humans. We placed a human point-light walker in optic flow fields that simulated forward motion of the observer. We introduced conflicts between biological features of the walker (i.e., facing and articulation) and the direction of scene-relative motion. We investigated whether perceived direction of scene-relative motion was biased towards biological features and compared the results to perceived direction of scene-relative motion of scrambled walkers and dot clouds. We found that for humans the perceived direction of scene-relative motion was biased towards biological features. Additionally, we found larger flow parsing gain for humans compared to the other walker types. This indicates that flow parsing is not the only visual mechanism relevant for estimating the direction of scene-relative motion of independently moving objects during self-motion: observers also rely on prior knowledge about typical object motion, such as typical facing and articulation of humans.

scholarly journals Processing of object motion and self-motion in the lateral subdivision of the medial superior temporal area in macaques

2019 ◽  
Vol 121 (4) ◽  
pp. 1207-1221 ◽  
Author(s):  
Ryo Sasaki ◽  
Dora E. Angelaki ◽  
Gregory C. DeAngelis
Keyword(s):  
Visual Cues ◽  
Moving Objects ◽  
Visual Motion ◽  
Object Motion ◽  
Image Motion ◽  
Motion Processing ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Visual Motion Processing ◽  
Self Motion

Multiple areas of macaque cortex are involved in visual motion processing, but their relative functional roles remain unclear. The medial superior temporal (MST) area is typically divided into lateral (MSTl) and dorsal (MSTd) subdivisions that are thought to be involved in processing object motion and self-motion, respectively. Whereas MSTd has been studied extensively with regard to processing visual and nonvisual self-motion cues, little is known about self-motion signals in MSTl, especially nonvisual signals. Moreover, little is known about how self-motion and object motion signals interact in MSTl and how this differs from interactions in MSTd. We compared the visual and vestibular heading tuning of neurons in MSTl and MSTd using identical stimuli. Our findings reveal that both visual and vestibular heading signals are weaker in MSTl than in MSTd, suggesting that MSTl is less well suited to participate in self-motion perception than MSTd. We also tested neurons in both areas with a variety of combinations of object motion and self-motion. Our findings reveal that vestibular signals improve the separability of coding of heading and object direction in both areas, albeit more strongly in MSTd due to the greater strength of vestibular signals. Based on a marginalization technique, population decoding reveals that heading and object direction can be more effectively dissociated from MSTd responses than MSTl responses. Our findings help to clarify the respective contributions that MSTl and MSTd make to processing of object motion and self-motion, although our conclusions may be somewhat specific to the multipart moving objects that we employed. NEW & NOTEWORTHY Retinal image motion reflects contributions from both the observer’s self-motion and the movement of objects in the environment. The neural mechanisms by which the brain dissociates self-motion and object motion remain unclear. This study provides the first systematic examination of how the lateral subdivision of area MST (MSTl) contributes to dissociating object motion and self-motion. We also examine, for the first time, how MSTl neurons represent translational self-motion based on both vestibular and visual cues.

scholarly journals Acoustic facilitation of object movement detection during self-motion

2011 ◽  
Vol 278 (1719) ◽  
pp. 2840-2847 ◽  
Author(s):  
F. J. Calabro ◽  
S. Soto-Faraco ◽  
L. M. Vaina
Keyword(s):  
Optic Flow ◽  
Animal Species ◽  
Moving Objects ◽  
Object Motion ◽  
Auditory Stimuli ◽  
Object Representations ◽  
Detection Rates ◽  
Object Movement ◽  
Psychophysical Studies ◽  
Self Motion

In humans, as well as most animal species, perception of object motion is critical to successful interaction with the surrounding environment. Yet, as the observer also moves, the retinal projections of the various motion components add to each other and extracting accurate object motion becomes computationally challenging. Recent psychophysical studies have demonstrated that observers use a flow-parsing mechanism to estimate and subtract self-motion from the optic flow field. We investigated whether concurrent acoustic cues for motion can facilitate visual flow parsing, thereby enhancing the detection of moving objects during simulated self-motion. Participants identified an object (the target) that moved either forward or backward within a visual scene containing nine identical textured objects simulating forward observer translation. We found that spatially co-localized, directionally congruent, moving auditory stimuli enhanced object motion detection. Interestingly, subjects who performed poorly on the visual-only task benefited more from the addition of moving auditory stimuli. When auditory stimuli were not co-localized to the visual target, improvements in detection rates were weak. Taken together, these results suggest that parsing object motion from self-motion-induced optic flow can operate on multisensory object representations.

scholarly journals A neural mechanism for detecting object motion during self-motion

2021 ◽  
Author(s):  
HyungGoo Kim ◽  
Dora Angelaki ◽  
Gregory DeAngelis
Keyword(s):  
Visual System ◽  
Moving Objects ◽  
Binocular Disparity ◽  
Motion Parallax ◽  
Neural Mechanism ◽  
Object Motion ◽  
Depth Cues ◽  
Behavioral Studies ◽  
Observer Motion ◽  
Self Motion

Detecting objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth, and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque area MT with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during selfmotion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

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