A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection (2203.02654v2)

Abstract: In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://github.com/alipay/VCSL.

• The paper introduces a large-scale VCSL dataset with 167,508 videos and 281,182 annotated segments to overcome previous dataset limitations.
• It proposes an innovative evaluation protocol that measures temporal precision and recall using inter-section IoU for realistic video pair assessment.
• Benchmark results show that spatio-temporal feature extraction methods like ViSiL and DINO improve detection, highlighting the need for refined local frame correspondence.

Segment-level Video Copy Detection: The VCSL Dataset and Evaluation Protocol

The paper "A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection" introduces VCSL (Video Copy Segment Localization), a new expansive dataset paired with an innovative evaluation protocol, both designed to improve and refine the video copy detection domain. Video copy detection is of paramount importance due to the increasing challenges posed by the ubiquity of both user-generated content (UGC) and professionally-generated content (PGC) on platforms like YouTube and Bilibili. This landscape fosters unsolicited, often transformative, duplication of content, necessitating more effective detection algorithms.

Dataset Overview

The VCSL dataset supersedes existing segment-level datasets, such as VCDB, by offering two orders of magnitude more data, with 167,508 videos and 281,182 annotated copied segments. A key differentiation of the VCSL dataset is its comprehensive real-world video segment annotations, making it the current largest dataset of its kind. This dataset pulls from realistic video copies and covers a plethora of video categories, ranging from movies, music videos, sports, to more niche categories like animation and daily life. This breadth facilitates a better training ground for models designed for segment-level video copy detection.

Evaluation Protocol and Metric Innovation

Accompanying the dataset is a novel evaluation protocol that refines the process by treating two entire videos as inputs rather than using a segment-based querying approach. This shift presents a more realistic scenario for practical tasks, where it is inherently uncertain which specific video segments are likely to be pirated. Previous metrics, like segment and frame-level precision and recall, typically focus on isolated segments, an approach that may overlook the intricacies of segment overlap accuracy.

The proposed metric in this paper evaluates precision and recall on both temporal axes of video pairs, considering the inter-section-over-union (IoU) for predicted video segments. Therefore, the metric is robust against diverse segment division equivalency and effectively reflects temporal correlation and alignment accuracy within these segments. This precision ensures a more robust analysis across different infringement scenarios and provides a tailored evaluation approach that can beneficially influence model tuning and performance assessments.

Performance Benchmark

The benchmark includes an evaluation of various feature extraction models and temporal alignment methodologies across the dataset. Four feature extraction methods—R-MAC, ViSiL, ViT, and DINO—offer insights into how frame features affect detection, with ViSiL and DINO showing improved performance due largely to their ability to learn spatio-temporal context. Additionally, five alignment methods—Hough Voting, Temporal Network (TN), Dynamic Programming (DP), Dynamic Time Warping (DTW), and Segment Pairwise Distance (SPD)—are analyzed, with SPD and TN performing strongly overall. However, distinct challenges remain, particularly in handling extensively edited infringed videos common in modern video-sharing environments.

Implications and Future Directions

The VCSL dataset and its evaluation protocol pave the way for advancements in segment-level video copy detection. By providing a substantial data resource and a nuanced evaluation framework, this research supports the development of more robust detection algorithms tailored for contemporary piracy challenges. Moving forward, there is scope for further investigation into feature representation strategies that better capture local frame correspondences under drastic transformations, as well as hybrid temporal alignment methods that can intelligently adapt to varying degrees of video complexity.

Ultimately, the research invites and enables an increased focus on developing methodologies that not only detect but also proficiently localize video copies, promoting enhanced content protection measures and, consequently, fostering a more ethical and lawful multimedia ecosystem.