结合级联卷积目标检测和跟踪的快速人头检测

Fast head detection combined with cascaded convolution object detection and tracking

针对图像序列中遮挡和角度变化等因素易造成人头漏检的情况,提出一种结合级联卷积目标检测模型MTCNN和核相关滤波(KCF)跟踪模型的快速人头检测方法,即MT-KCF人头检测模型。利用批次归一化改进MTCNN网络,采用级联的方式检测人头;将检测到的人头位置信息输入到KCF跟踪模型中,对人头目标进行快速稳定的跟踪;为确保持续稳定地检测到人头,在跟踪多帧后,再次利用检测模型重新对人头进行检测。实验结果表明:MT-KCF模型在图像序列中具有较高的检测精度和较快的检测速度,平均准确率为94.85%,在640×480大小的图像序列中平均速度为108帧/s。

Aiming at the situation that factors such as occlusion and angle change in image sequences are easy to cause head to miss the detection,a fast human head detection method is proposed which combined the cascade convolution object detection model MTCNN and kernel correlation filtering(KCF)tracking model,namely MT-KCF head detection model.Firstly,the batch normalization is used to improve the MTCNN network,and the human head is detected by cascading.Secondly,the detected human head coordinate information is input into the KCF tracking model,and the head object is quickly and steadily tracked.Finally,to ensure that the human head is detected continuously and steadily,the human head is detected again using the detection model after tracking several frames.Experimental results show that the MT-KCF model has higher detection precision and faster detection speed in image sequences,and the average accuracy is 94.85%.The average speed in the 640×480 image sequences is 108 frames per second respectively.