Summarization of User-Generated Sports Video by Using Deep Action Recognition Features

Tejero-de-Pablos, Antonio; Nakashima, Yuta; Sato, Tomokazu; Yokoya, Naokazu; Linna, Marko; Rahtu, Esa

doi:10.1109/tmm.2018.2794265

Cited by 76 publications

(51 citation statements)

References 51 publications

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“…Highlights generated by the system were compared with the highlights telecasted by the official broadcaster of the match. Tejero-De-Prablos [19] presented a scheme for user-generated sports video summarization and employed the LSTM model to identify various user actions in the video. Videos were classified into interesting or non-interesting categories.…”

Section: Scene Classification Via Deep-learning Approachmentioning

confidence: 99%

Scene Classification for Sports Video Summarization Using Transfer Learning

Rafiq

Agyeman

et al. 2020

Sensors

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This paper proposes a novel method for sports video scene classification with the particular intention of video summarization. Creating and publishing a shorter version of the video is more interesting than a full version due to instant entertainment. Generating shorter summaries of the videos is a tedious task that requires significant labor hours and unnecessary machine occupation. Due to the growing demand for video summarization in marketing, advertising agencies, awareness videos, documentaries, and other interest groups, researchers are continuously proposing automation frameworks and novel schemes. Since the scene classification is a fundamental component of video summarization and video analysis, the quality of scene classification is particularly important. This article focuses on various practical implementation gaps over the existing techniques and presents a method to achieve high-quality of scene classification. We consider cricket as a case study and classify five scene categories, i.e., batting, bowling, boundary, crowd and close-up. We employ our model using pre-trained AlexNet Convolutional Neural Network (CNN) for scene classification. The proposed method employs new, fully connected layers in an encoder fashion. We employ data augmentation to achieve a high accuracy of 99.26% over a smaller dataset. We conduct a performance comparison against baseline approaches to prove the superiority of the method as well as state-of-the-art models. We evaluate our performance results on cricket videos and compare various deep-learning models, i.e., Inception V3, Visual Geometry Group (VGGNet16, VGGNet19) , Residual Network (ResNet50), and AlexNet. Our experiments demonstrate that our method with AlexNet CNN produces better results than existing proposals.

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Section: Scene Classification Via Deep-learning Approachmentioning

confidence: 99%

Scene Classification for Sports Video Summarization Using Transfer Learning

Rafiq

Agyeman

et al. 2020

Sensors

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“…Each technique summarizes the video using a specific feature, such as trajectories, moving objects, abnormal detection, and many others. These categories of techniques can be classified into two general categories, scene-based (i.e., static [1-7, 9, 17, 21, 22], dynamic [8,12,13,16,[18][19][20] and content-based approaches, and the content-based approaches can be further decomposed into three types related to the content of the video including motion-based [10][11][12][13][14][15][16]20], action-based [21,22] and event-based [11,15,[17][18][19], as shown in Fig. 1.…”

Section: Related Workmentioning

confidence: 99%

“…The authors chose games to test the performance of the methods because they contain a succession of complex actions. The extended version of this approach uses deep neural networks to extract two types of action-related features and classify video segments into interesting or uninteresting parts [22]. The authors proposed a method to recognize actions, which can lead to a good selection of meaningful informative summaries.…”

Section: Related Workmentioning

confidence: 99%

“…Content-based video summarization is based on video content that requires pre-processing, for example, motionbased methods utilize the results of motion detection or the trajectories of objects to summarize the video. In addition, the summarization using this technique can be static(keyframes) or dynamic(short clips) [15][16][17][18][19][20][21][22]. The challenge in video summarization is related to the video understanding and the classification of important sequences of the video, and the importance depends on the types of actions or objects that must be summarized.…”

Section: Introductionmentioning

confidence: 99%

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A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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“…Segmenting events in sports is a sub-class of video summarising problem [8], [14]. Researchers in the past made progress in segmenting events for sports such as baseball [10], tennis [6], cricket [7], football [4], [11], [1] rugby [2].…”

Section: Related Workmentioning

confidence: 99%

Asking Graphs "How Did I Play?" Generating Graphs through Images Via Signals

Maram

Jain

2020

Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports

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Cricket is a game that requires players to constantly adapt to situations and customize their game depending on opponents and playing conditions. Players and coaching staff often watch video clips to understand the strategies of opponents. Iterating through multiple matches over many years across various leagues and formats, and extracting clips is a tiring process. In this paper, we propose a computer vision framework to segment cricket matches into clips based on context and construct real-time graphs using meta-data from segmented clips. We discuss various queries on the generated graphs and also evaluate our segmentation and querying model based on the accuracy and quality of the retrieved data. CCS CONCEPTS • Information systems → Multimedia streaming; • Human-centered computing → Visual analytics.

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Summarization of User-Generated Sports Video by Using Deep Action Recognition Features

Cited by 76 publications

References 51 publications

Scene Classification for Sports Video Summarization Using Transfer Learning

Scene Classification for Sports Video Summarization Using Transfer Learning

A combined multiple action recognition and summarization for surveillance video sequences

Asking Graphs "How Did I Play?" Generating Graphs through Images Via Signals

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