Video Scene Recognition System

1983 ◽  
Author(s):  
Robert Y. Wong ◽  
Rashid M. Sallak
Keyword(s):  
Recognition System ◽  
Scene Recognition ◽  
Video Scene

scholarly journals Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1718
Author(s):  
Chien-Hsing Chou ◽  
Yu-Sheng Su ◽  
Che-Ju Hsu ◽  
Kong-Chang Lee ◽  
Ping-Hsuan Han
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
User Experience ◽  
Recognition System ◽  
Scene Recognition ◽  
Single Shot ◽  
Auditory Signals ◽  
Hot Weather ◽  
Viewing Experience ◽  
At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

Horror Video Scene Recognition Based on Multi-view Multi-instance Learning

2013 ◽  
pp. 599-610 ◽  
Author(s):  
Xinmiao Ding ◽  
Bing Li ◽  
Weiming Hu ◽  
Weihua Xiong ◽  
Zhenchong Wang
Keyword(s):  
Scene Recognition ◽  
Video Scene

scholarly journals Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5185
Author(s):  
Yu Zhai ◽  
Jieyu Lei ◽  
Wenze Xia ◽  
Shaokun Han ◽  
Fei Liu ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Super Resolution ◽  
Recognition System ◽  
Scene Recognition ◽  
Image Texture ◽  
Laser Radar ◽  
Range Image ◽  
Range Images ◽  
Model Library

This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.

Attention based consistent semantic learning for micro-video scene recognition

2021 ◽  
Vol 543 ◽  
pp. 504-516
Author(s):  
Jie Guo ◽  
Xiushan Nie ◽  
Yuling Ma ◽  
Kashif Shaheed ◽  
Inam Ullah ◽  
...  
Keyword(s):  
Scene Recognition ◽  
Semantic Learning ◽  
Video Scene

scholarly journals Digital Audio Scene Recognition Method Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sihua Sun
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Scene Recognition ◽  
Learning Technology ◽  
Digital Audio ◽  
Recognition Method ◽  
Feature Extraction Method ◽  
Auditory Scene ◽  
Experimental Data Analysis

Audio scene recognition is a task that enables devices to understand their environment through digital audio analysis. It belongs to a branch of the field of computer auditory scene. At present, this technology has been widely used in intelligent wearable devices, robot sensing services, and other application scenarios. In order to explore the applicability of machine learning technology in the field of digital audio scene recognition, an audio scene recognition method based on optimized audio processing and convolutional neural network is proposed. Firstly, different from the traditional audio feature extraction method using mel-frequency cepstrum coefficient, the proposed method uses binaural representation and harmonic percussive source separation method to optimize the original audio and extract the corresponding features, so that the system can make use of the spatial features of the scene and then improve the recognition accuracy. Then, an audio scene recognition system with two-layer convolution module is designed and implemented. In terms of network structure, we try to learn from the VGGNet structure in the field of image recognition to increase the network depth and improve the system flexibility. Experimental data analysis shows that compared with traditional machine learning methods, the proposed method can greatly improve the recognition accuracy of each scene and achieve better generalization effect on different data.

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