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Object as Point

1. Motivation:

• Traditional detector need post-processing (NMS), hard to differentiate and train (not end-to-end).
• One-stage detector: sliding anchors over imgs and classify them
• Two-stage detector: recompute imgs features for potential boxs and classify them.

2. Methods:

2.1 Highlight:

• CenterNet:
  • represent object by a single point of bbox
  • regress object size etc.
• Inference:
  • single network forward-pass without NMS

2.2 Implement

• Points of bbox:

  • generate heat map with a FCN (keypoint prediction network)

  • extract local peaks in the key point

    • background: CornerNet etc. use keypoint estimation to detect corner

• Size (via regression):

  • image features at each peak predict the objects Bboxs

The network predicts a total of C + 4 outputs (C classes, 2 para for local offsets, 2 para for size) at each location. All output share a common FC backbone network. For each modality, the features of the backbone are then passed through a separate 3 3 convolution, ReLu and another 1 1 convolutions.

output stride R = 4, i.e. the resolution will reduce to be 4 times smaller

2.3 Loss

A weighted sum of 3 loss

• $L_k$: penalty-reduced pixel wise logistic regression with focall loss
• $L_{off}$: additionally predict a local offset,
  • to recover the discretization error cases by the output stride
  • each center point has 2 offset
• $L_{size}$: regress object size for each object