Summarizing the performances of a background subtraction algorithm measured on several videos

• URL: http://arxiv.org/abs/2002.05654v2
• Date: Thu, 28 May 2020 15:55:54 GMT
• Title: Summarizing the performances of a background subtraction algorithm measured on several videos
• Authors: S\'ebastien Pi\'erard and Marc Van Droogenbroeck
• Abstract summary: We present a theoretical approach to summarize the performances for multiple videos. We also give formulas and an algorithm to calculate summarized performances. We showcase our observations on CDNET 2014.
• Score: 9.440689053774898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to average them hierarchically, within a category first, then between categories, a procedure which we name "summarization". While the summarization by averaging performance indicators is a valuable effort to standardize the evaluation procedure, it has no theoretical justification and it breaks the intrinsic relationships between summarized indicators. This leads to interpretation inconsistencies. In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators. In addition, we give formulas and an algorithm to calculate summarized performances. Finally, we showcase our observations on CDNET 2014.

Related papers

• Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval [80.09819072780193]
  Average Precision (AP) assesses the overall rankings of relevant videos at the top list. Recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally.
  arXiv  Detail & Related papers  (2024-07-22T11:52:04Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• 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)
• Transfer of Representations to Video Label Propagation: Implementation Factors Matter [31.030799003595522]
  We study the impact of important implementation factors in feature extraction and label propagation. We show that augmenting video-based correspondence cues with still-image-based ones can further improve performance. We hope that this study will help to improve evaluation practices and better inform future research directions in temporal correspondence.
  arXiv  Detail & Related papers  (2022-03-10T18:58:22Z)
• Video Summarization Based on Video-text Modelling [0.0]
  We propose a multimodal self-supervised learning framework to obtain semantic representations of videos. We also introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. An objective evaluation framework is proposed to measure the quality of video summaries based on video classification.
  arXiv  Detail & Related papers  (2022-01-07T15:21:46Z)
• Unsupervised Video Summarization via Multi-source Features [4.387757291346397]
  Video summarization aims at generating a compact yet representative visual summary that conveys the essence of the original video. We propose the incorporation of multiple feature sources with chunk and stride fusion to provide more information about the visual content. For a comprehensive evaluation on the two benchmarks TVSum and SumMe, we compare our method with four state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-26T13:12:46Z)
• Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation [96.67525775629444]
  Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video.
  arXiv  Detail & Related papers  (2021-03-20T23:30:01Z)
• On Semantic Similarity in Video Retrieval [31.61611168620582]
  We propose a move to semantic similarity video retrieval, where multiple videos/captions can be deemed equally relevant. Our analysis is performed on three commonly used video retrieval datasets (MSR-VTT, YouCook2 and EPIC-KITCHENS)
  arXiv  Detail & Related papers  (2021-03-18T09:12:40Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Unsupervised Learning of Video Representations via Dense Trajectory Clustering [86.45054867170795]
  This paper addresses the task of unsupervised learning of representations for action recognition in videos. We first propose to adapt two top performing objectives in this class - instance recognition and local aggregation. We observe promising performance, but qualitative analysis shows that the learned representations fail to capture motion patterns.
  arXiv  Detail & Related papers  (2020-06-28T22:23:03Z)

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.