我将使用 Navneet Dalal、Bill Triggs 和 Cordelia Schmid 在 2006 年提出的方法检测物体(使用定向直方图的流量和外观进行人体检测)
在那种情况下，他们首先使用 HOG 算法计算外观描述符，并使用光流计算运动描述符。然后他们将这两个描述符结合起来得到我理解的最终描述符。但我找不到他们是如何结合的。
所以我的问题是如何结合外观和运动描述符来得到最终的描述符。 (我将使用更精简的 SVM 进行训练，并使用 opencv 来实现)
最佳答案
在论文第 12 页中提到:
The combined-feature detectors above are monolithic – they concatenate the motion and appearance features into a single large feature vector and train a combined classifier on it.
因此，您只需将两个描述符串联起来即可构成一个特征向量。其他提到的可能性是专家混合:
In our experiments these effects mitigate the losses due to separate training and the linear Mixture of Experts classifier actually performs slightly better than the best monolithic detector. For now the differences are marginal (less than 1%), but the Mixture of Experts architecture provides more flexibility and may ultimately be preferable. The component classifiers could also be combined in a more sophisticated way, for example using a rejection cascade [1, 22, 21] to improve the runtime.
您可以阅读有关此方法的信息，例如 here .
关于opencv - 如何结合运动和外观描述符，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/19790425/