Vicente Enrique Machaca Arceda, Elian Laura Riveros
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引用次数: 24
摘要
在这项工作中，我们的目标是在视频中检测车祸。我们提出了一个三阶段框架:第一个是使用卷积神经网络的汽车检测方法，在这种情况下，我们使用了网络You Only Look Once (YOLO);第二阶段是跟踪器，以聚焦每辆车;然后在最后阶段，我们使用暴力流(ViF)描述符和支持向量机(SVM)来检测每辆车的碰撞。我们的提议几乎是实时的，只有0.5秒的延迟，而且我们检测车祸的准确率达到了89%。
In this work, we aim to detect car crash accidents in video. We propose a three-stage framework: The first one is a car detection method using convolutional neural networks, in this case, we used the net You Only Look Once (YOLO); the second stage is a tracker in order to focus each car; then the final stage for each car we use the Violent Flow (ViF) descriptor with a Support Vector Machine (SVM) in order to detect the car crashes. Our proposal is almost in real time with just 0.5 seconds of delay and also we got a 89% accuracy detecting car crashes.
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