用深度学习检测视频中特定人物，提升安防系统识别效率。
An Approach for Detection of Entities in Dynamic Media Contents
- 基于监督学习构建神经网络，从简单特征定位目标人物。
- 在安哥拉公共安全场景下，实现对失踪者、罪犯等的高效追踪。
- 适用于政府安防系统，尤其适合大规模视频数据筛查。
本文提出一种在视频序列中搜索与检测指定实体的方法。研究聚焦于利用人工神经网络的深度学习技术，实现对视频中特定人物的检测。该任务因视频中存在大量干扰对象而具有挑战性。实验结果表明，相较于现有方法，本方法通过结构化监督学习算法，能够有效利用目标人物的简单特征，在私有或公共图像库中高效定位特定个体。以安哥拉为例，所提出的分类器可支持国家安保系统，结合目标人员数据库（如失踪者、罪犯等）与集成公共安全部门（CISP）的视频数据，增强对可疑人员的实时监控能力。
原文摘要 · Abstract (English)
The notion of learning underlies almost every evolution of Intelligent Agents. In this paper, we present an approach for searching and detecting a given entity in a video sequence. Specifically, we study how the deep learning technique by artificial neuralnetworks allows us to detect a character in a video sequence. The technique of detecting a character in a video is a complex field of study, considering the multitude of objects present in the data under analysis. From the results obtained, we highlight the following, compared to state of the art: In our approach, within the field of Computer Vision, the structuring of supervised learning algorithms allowed us to achieve several successes from simple characteristics of the target character. Our results demonstrate that is new approach allows us to locate, in an efficient way, wanted individuals from a private or public image base. For the case of Angola, the classifier we propose opens the possibility of reinforcing the national security system based on the database of target individuals (disappeared, criminals, etc.) and the video sequences of the Integrated Public Security Centre (CISP).
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