| Full text | |
| Author(s): |
Peixoto, Bruno
;
Lavi, Bahram
;
Pereira Martin, Joao Paulo
;
Avila, Sandra
;
Dias, Zanoni
;
Rocha, Anderson
;
IEEE
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
Violence detection in videos aims to identify whether a violent action occurred within a video stream. Effective tools for intelligent video analysis are highly demanded, specially to determine violence in video streams. Such solution could have applications in detecting inappropriate behaviors in video feeds, aiding law-enforcement in forensic cases, protecting children from accessing inappropriate online content and helping parents making informed decisions about what their kids should watch. Prior art on violence detection, particularly recently proposed deep learning based ones, seeks to identify violence in videos as a whole, without considering breaking down the subject into some of its underlying concepts. In this paper, we explore a different methodology of violence detection, which relies upon two deep neural network (DNNs) frameworks to learn spatial-temporal information on video clips under different scenarios subjective- and conceptual-based. We leverage deep feature representations for each specific concept, and aggregate them by training a shallow neural network as a binary-classification problem to describe violence as a whole. Finally, we show that using more specific concepts is an intuitive and effective solution, besides being complementary to form a more robust definition of violence. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points |
| Grantee: | Flávio Keidi Miyazawa |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 17/16246-0 - Sensitive media analysis through deep learning architectures |
| Grantee: | Sandra Eliza Fontes de Avila |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/16871-1 - Problems of sorting permutations by fragmentation-weighted operations |
| Grantee: | Alexsandro Oliveira Alexandrino |
| Support Opportunities: | Scholarships in Brazil - Master |