matching策略中，对re-id应该默认采用cosine相似度，为什么代码里写的是欧式距离

[Bilibilee]

matching.py

from scipy.spatial.distance import cdist

def embedding_distance(tracks, detections, metric='cosine'): cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) if cost_matrix.size == 0: return cost_matrix det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float) track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float) cost_matrix = np.maximum(0.0, cdist(track_features, det_features)) # cdist默认是求欧式距离 return cost_matrix

[Zhongdao]

The embedding is L2 normalized (see here), and so Euclidean distance is equivalent to cosine distance.