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Video content analysis is an active research domain due to the availability and the increment of audiovisual data in the digital format. There is a need to automatically extracting video content for efficient access, understanding, browsing and retrieval of videos. To obtain the information that is of interest and to provide better entertainment, tools are needed to help users extract relevant content and to effectively navigate through the large amount of available video information. Existing methods do not seem to attempt to model and estimate the semantic content of the video. Detecting and interpreting human presence, actions and activities is one of the most valuable functions in this proposed framework. The general objectives of this research are to analyze and process the audio-video streams to a robust audiovisual action recognition system by integrating, structuring and accessing multimodal information via multidimensional retrieval and extraction model. The proposed technique characterizes the action scenes by integrating cues obtained from both the audio and video tracks. Information is combined based on visual features (motion, edge, and visual characteristics of objects), audio features and video for recognizing action. This model uses HMM and GMM to provide a framework for fusing these features and to represent the multidimensional structure of the framework. The action-related visual cues are obtained by computing the spatiotemporal dynamic activity from the video shots and by abstracting specific visual events. Simultaneously, the audio features are analyzed by locating and compute several sound effects of action events that embedded in the video. Finally, these audio and visual cues are combined to identify the action scenes. Compared with using single source of either visual or audio track alone, such combined audiovisual information provides more reliable performance and allows us to understand the story content of movies in more detail. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
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VIDEO OBJECTS DESCRIPTION IN HINDI TEXT LANGUAGE
Video activity recognition has grown to be a dynamic location of analysis in latest years. A widespread
information-driven approach is denoted in this paper that produces descriptions of video content into
textual content description inside the Hindi language. This method combines the final results of modern
item with "real-international" records to pick the in all subject-verb-object triplet for depicting a video. The
usage of this triplet desire technique, a video is tagged via the trainer, mainly, Subject, Verb, and object
(SVO) and then this data is mined to improve the result of checking out video clarification by using pastime
as well as item identity. Contrasting preceding approaches, this method can annotate arbitrary videos
deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
work affords initial and primary text description within the Hindi language that is producing easy words
and sentence formation. But the fundamental challenging attempt on this work is to extract grammatically
accurate and expressive text records in Hindi textual content regarding video content.
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deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
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Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on recognizing videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning and knowledge graph techniques, researchers in multiple communities are now striving to bridge videos with natural language in order to move beyond classification to interpretation, which should be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques, including video entity linking, video-language alignment, and video captioning, and discuss how domain knowledge can fit in to improve the performance.
Video + Language: Where Does Domain Knowledge Fit in?
Does deep learning solve all the machine learning problems? Where would domain knowledge fit in? While it is common in medical data analytics to incorporate domain knowledge, we focus on one emerging area in computer vision and language processing, video+language, to answer these questions.
Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on recognizing videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning and knowledge graph techniques, researchers in multiple communities are now striving to bridge videos with natural language in order to move beyond classification to interpretation, which should be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques, including video entity linking, video-language alignment, and video captioning, and discuss how domain knowledge can fit in to improve the performance.
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GridMate - End to end testing is a critical piece to ensure quality and avoid...
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
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Monitoring Java Application Security with JDK Tools and JFR Events
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Fcv scene schmid
- 1. Action recognition in videos Cordelia Schmid INRIA Grenoble
- 4. TRECVID - Multimedia Event Detection Attempting a board trick Feeding an animal Wedding ceremony Getting a vehicle unstuck
- 12. Action recognition in videos Motion history image [Bobick & Davis, 2001] Spatial motion descriptor [Efros et al. ICCV 2003] Learning dynamic prior [Blake et al. 1998] Sign language recognition [Zisserman et al. 2009]