Shot boundary detection method based on a new extensive dataset and
mixed features
- URL: http://arxiv.org/abs/2109.01057v1
- Date: Thu, 2 Sep 2021 16:19:24 GMT
- Title: Shot boundary detection method based on a new extensive dataset and
mixed features
- Authors: Alexander Gushchin, Anastasia Antsiferova and Dmitriy Vatolin
- Abstract summary: Shot boundary detection in video is one of the key stages of video data processing.
New method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Shot boundary detection in video is one of the key stages of video data
processing. A new method for shot boundary detection based on several video
features, such as color histograms and object boundaries, has been proposed.
The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2]
datasets, and the MSU CC datasets, based on videos used in the video codec
comparison conducted at MSU, as well as videos from the IBM set, were also
plotted. The total dataset for algorithm development and testing exceeded the
known TRECVID datasets. Based on the test results, the proposed algorithm for
scene change detection outperformed its counterparts with a final F-score of
0.9794.
Related papers
- ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection [51.16181295385818]
We first collect an annotated RGB-D video SODOD (DSOD-100) dataset, which contains 100 videos within a total of 9,362 frames.
All the frames in each video are manually annotated to a high-quality saliency annotation.
We propose a new baseline model, named attentive triple-fusion network (ATF-Net) for RGB-D salient object detection.
arXiv Detail & Related papers (2024-06-18T12:09:43Z) - A Deep Learning Approach to Video Anomaly Detection using Convolutional
Autoencoders [0.0]
Our method utilizes a convolutional autoencoder to learn the patterns of normal videos and then compares each frame of a test video to this learned representation.
We evaluated our approach and achieved an overall accuracy of 99.35% on the Ped1 dataset and 97% on the Ped2 dataset.
The results show that our method outperforms other state-of-the-art methods and it can be used in real-world applications for video anomaly detection.
arXiv Detail & Related papers (2023-11-07T21:23:32Z) - Glitch in the Matrix: A Large Scale Benchmark for Content Driven
Audio-Visual Forgery Detection and Localization [20.46053083071752]
We propose and benchmark a new dataset, Localized Visual DeepFake (LAV-DF)
LAV-DF consists of strategic content-driven audio, visual and audio-visual manipulations.
The proposed baseline method, Boundary Aware Temporal Forgery Detection (BA-TFD), is a 3D Convolutional Neural Network-based architecture.
arXiv Detail & Related papers (2023-05-03T08:48:45Z) - Video Segmentation Learning Using Cascade Residual Convolutional Neural
Network [0.0]
We propose a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process.
Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach.
arXiv Detail & Related papers (2022-12-20T16:56:54Z) - Mitigating Representation Bias in Action Recognition: Algorithms and
Benchmarks [76.35271072704384]
Deep learning models perform poorly when applied to videos with rare scenes or objects.
We tackle this problem from two different angles: algorithm and dataset.
We show that the debiased representation can generalize better when transferred to other datasets and tasks.
arXiv Detail & Related papers (2022-09-20T00:30:35Z) - Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in
VIS and NIR Scenario [87.72258480670627]
Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum compared to the real images.
This paper proposes a Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spatial-temporal feature representation.
arXiv Detail & Related papers (2022-07-05T09:27:53Z) - An End-to-End Trainable Video Panoptic Segmentation Method
usingTransformers [0.11714813224840924]
We present an algorithm to tackle a video panoptic segmentation problem, a newly emerging area of research.
Our proposed video panoptic segmentation algorithm uses the transformer and it can be trained in end-to-end with an input of multiple video frames.
The method archived 57.81% on the KITTI-STEP dataset and 31.8% on the MOTChallenge-STEP dataset.
arXiv Detail & Related papers (2021-10-08T10:13:37Z) - Robust Unsupervised Video Anomaly Detection by Multi-Path Frame
Prediction [61.17654438176999]
We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design.
Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
arXiv Detail & Related papers (2020-11-05T11:34:12Z) - A New Unified Method for Detecting Text from Marathon Runners and Sports
Players in Video [37.86508176161514]
The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions.
Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences.
A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods.
arXiv Detail & Related papers (2020-05-26T05:54:28Z) - UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional
Variational Autoencoders [81.5490760424213]
We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
arXiv Detail & Related papers (2020-04-13T04:12:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.