Attitude Tracker "Pose flow:efficient Online Pose Tracking"

Multi-person joint attitude tracking under complex scenes, the paper designed an on-line optimization network to establish the frame attitude relationship, and formed the attitude flow, in addition, designed a attitude flow non-maximum value suppression to reduce redundant attitude flow, re-establish the relationship between the time of the non-intersecting attitude. Experiment on two attitude Tracking database pose Tracking datasets
([Iqbal et al.] and [Girdhar et al., 2017]) contrast.

There are two kinds of attitude tracking methods, top-down and bottom-up. The top-down approach first detects body bbox, estimates key points, and tracks box. The bottom-up method detects the candidate each frame, establishes the space-time map, forms each person's attitude trajectory. The top-down approach is more promising, but obscured, truncated, blurred images, and so on, as shown in the following figure, which requires sharing information between frames.

* * Method Structure * *

Two-step, pose flow building and pose flow NMS

* * Basic concept * *

1. In-frame attitude distance

To measure the similarity of two gestures in the same frame, the soft match function is:

Where $c_1,c_2$ is the key point of Attitude $p_1,p_2$, the direct spatial distance of the key points is:

2. Inter-frame attitude distance using orb matching calculation

**pose Flow building**

Attitude estimation establishes the relationship of the same person between frames, and, for a certain gesture, creates a candidate-associated attitude set in subsequent frames, i.e.:

Where DC is the orb distance. For the From T to (t+t) frame, maximize the target function:

Among them, S (p) is the confidence score of posture P, which is related to box score and key point score.

**pose Flow nms**

1.Pose Flow Distance

For any two attitude flows, extract the sub-flows that intersect their time, and the distance of two attitude flows is:

where DF () is the intra-frame attitude distance

2. Attitude Flow Fusion

The attitude flow with the maximum confidence score as a reference, by using DPF (), fusing other flows close to the reference attitude flow, forming a new attitude flow, representing the attitude flow, the key points representing the attitude flow 2D coordinates and the confidence score are:

The schematic diagram of the attitude Flow NMS is:

* * Experimental results * *