结合关联特征和卷积神经网络的视频目标检测

Video Object Detection Based on Correlation Feature and Convolutional Neural Network

针对视频目标检测领域中使用图像检测算法存在的速度与精度相互制约的问题,为充分利用目标在帧之间的运动信息,提出一种结合关联特征和卷积神经网络的视频检测方法.首先,当前视频帧使用图像检测算法提取特征,其次,利用两帧的关联特征预测当前帧的特征图,最后,使用关联特征中的运动信息来修正最终结果.本文的方法最终在ImageNet数据集上进行了实验,结果比当前方法获得了较好的精度提升,同时保持了较快的速度.

The problem of mutual restriction between speed and precision caused by using image detection algorithm in the field of video object detection,a video detection method based on correlation features and convolutional neural network is proposed in order to make full use of the target s motion between frames.Our methods are demonstrated as follows:firstly,an image detection algorithm is used to extract features from the current video frame;secondly,the correlation features between the frames is employed to predict the feature maps of the current frame and finally,the target motion information from the associated features is used to predict the final result.The method proposed in this paper finally experimented on the ImageNet dataset,which is proved better than the current method since the precision is enhanced and a faster speed is maintained.