딥러닝을 이용한 군 내외 거수자 행동 인식: 키포인트 2D 스케일링을 중심으로
Suspicious behavior recognition using deep learning: Focusing on keypoint 2D scaling
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초록
본 연구는 딥러닝을 통해 군 내외에서 거수자의 행동을 인식하여 국방 보안 체계를 강화하는 데 목적을 두고 있다. 감시 카메라는 범죄자, 수상한 행동을 하는 사람을 찾아내는데 도움이 된다. 하지만 관리자가 카메라에서 전송되는 많은 영상을 모두 모니터링해야 한다는 점에서 비효율적이다. 큰 비용이 발생하며, 인적 오류에 취약하다. 따라서 본 연구에서는 감시카메라 영상만으로 주의 있게 봐야 할 행동을 하는 사람을 찾아내는 방법을 제안한다. 이를 위해 거수자 영상 데이터를 수집하였다. 또한, 입력 영상에서 사람마다 다른 신장, 동작을 일반화하는 알고리즘을 적용한 후 CNN, 양방향 LSTM, DNN을 결합한 모델을 통해 학습하였다. 실험 결과, 거수자의 행동 인식의 정확도를 향상시켰다. 따라서 기존 감시 카메라에 딥러닝을 접목한다면 거수자를 효율적으로 찾아낼 수 있을 것으로 기대한다.
Abstract
The purpose of this study is to reinforce the defense and security system by recognizing the behaviors of suspicious person both inside and outside the military using deep learning. Surveillance cameras help detect criminals and people who are acting unusual. However, it is inefficient in that the administrator must monitor all the images transmitted from the camera. It incurs a large cost and is vulnerable to human error. Therefore, in this study, we propose a method to find a person who should be watched carefully only with surveillance camera images. For this purpose, the video data of doubtful behaviors were collected. In addition, after applying a algorithm that generalizes different heights and motions for each person in the input images, we trained through a model combining CNN, bidirectional LSTM, and DNN. As a result, the accuracy of the behavior recognition of suspicious behaviors was improved. Therefore, if deep learning is applied to existing surveillance cameras, it is expected that it will be possible to find the dubious person efficiently.
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