scholarly journals Identifying images on moving objects to enhance the recognition

2017 ◽  
Vol 7 (1.5) ◽  
pp. 279
Author(s):  
C. Raghavendra ◽  
A. Kumaravel ◽  
S. Sivasubramanian
Keyword(s):  
Object Recognition ◽  
Moving Objects ◽  
Moving Pictures ◽  
New Procedures

To explore another group of algorithms that break down time-changing scenes, perceiving and following educated questions after some time. The new procedures are wanted to address key request of moving pictures, including capricious moment to-minute changes in region, gauge, presentation, lighting, and obstacle. We exhibit a novel endeavour in which objects turn and divert while suspended from a flexible's arms;the identification and following calculation joins attributes of various earlier distributed strategies, consolidating them in a novel mould to empower this recently presented assignment. Different strategies have discovered that enhancing recognition will enhance following; we demonstrate that enhanced following enhances object recognition.

scholarly journals Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

2020 ◽  
Vol 12 (12) ◽  
pp. 1908
Author(s):  
Tzu-Yi Chuang ◽  
Jen-Yu Han ◽  
Deng-Jie Jhan ◽  
Ming-Der Yang
Keyword(s):  
Object Recognition ◽  
Moving Objects ◽  
Low Cost ◽  
Three Dimensional ◽  
Real Life ◽  
Moving Object ◽  
Camera Motion ◽  
Ptz Camera ◽  
Real Life Data ◽  
Close Range

Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost.

scholarly journals Spontaneous Preference for Slowly Moving Objects in Visually Naïve Animals

Open Mind ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 111-122 ◽  
Author(s):  
Justin N. Wood
Keyword(s):  
Object Recognition ◽  
Visual Experience ◽  
Moving Objects ◽  
Effect Sizes ◽  
Rearing Method ◽  
The World ◽  
Visual Preferences ◽  
Controlled Rearing ◽  
Rotating Objects

To perceive the world successfully, newborns need certain types of visual experiences. The development of object recognition, for example, requires visual experience with slowly moving objects. To date, however, it is unknown whether newborns actively seek out the best visual experiences for developing object recognition. To address this question, I used an automated controlled-rearing method to examine whether visually naïve animals (newborn chicks) seek out slowly moving objects. Despite receiving equal exposure to slowly and to quickly rotating objects, the majority of the chicks developed a preference for slowly rotating objects. This preference was robust, producing large effect sizes across objects, experiments, and successive test days. These results indicate that newborn brains rapidly develop mechanisms for orienting young animals toward optimal visual experiences, thus facilitating the development of object recognition. This study also demonstrates that automation can be a valuable tool for studying the origins and development of visual preferences.

scholarly journals A Manufacturing-Oriented Intelligent Vision System Based on Deep Neural Network for Object Recognition and 6D Pose Estimation

2021 ◽  
Vol 14 ◽  
Author(s):  
Guoyuan Liang ◽  
Fan Chen ◽  
Yu Liang ◽  
Yachun Feng ◽  
Can Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Pose Estimation ◽  
Manufacturing Industry ◽  
Deep Neural Network ◽  
Moving Objects ◽  
Vision System ◽  
Superior Performance ◽  
Feature Extraction Method ◽  
Benchmark Datasets

Nowadays, intelligent robots are widely applied in the manufacturing industry, in various working places or assembly lines. In most manufacturing tasks, determining the category and pose of parts is important, yet challenging, due to complex environments. This paper presents a new two-stage intelligent vision system based on a deep neural network with RGB-D image inputs for object recognition and 6D pose estimation. A dense-connected network fusing multi-scale features is first built to segment the objects from the background. The 2D pixels and 3D points in cropped object regions are then fed into a pose estimation network to make object pose predictions based on fusion of color and geometry features. By introducing the channel and position attention modules, the pose estimation network presents an effective feature extraction method, by stressing important features whilst suppressing unnecessary ones. Comparative experiments with several state-of-the-art networks conducted on two well-known benchmark datasets, YCB-Video and LineMOD, verified the effectiveness and superior performance of the proposed method. Moreover, we built a vision-guided robotic grasping system based on the proposed method using a Kinova Jaco2 manipulator with an RGB-D camera installed. Grasping experiments proved that the robot system can effectively implement common operations such as picking up and moving objects, thereby demonstrating its potential to be applied in all kinds of real-time manufacturing applications.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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