  Occlusion issue is the biggest challenge in pedestrian detection. RCNN-based
detectors extract instance features by cropping rectangle regions of interest
in the feature maps. However, the visible pixels of the occluded objects are
limited, making the rectangle instance feature mixed with a lot of
instance-irrelevant noise information. Besides, by counting the number of
instances with different degrees of overlap of CrowdHuman dataset, we find that
the number of severely overlapping objects and the number of slightly
overlapping objects are unbalanced, which may exacerbate the challenges posed
by occlusion issues. Regarding to the noise issue, from the perspective of
denoising, an iterable dynamic instance noise filter (DINF) is proposed for the
RCNN-based pedestrian detectors to improve the signal-noise ratio of the
instance feature. Simulating the wavelet denoising process, we use the instance
feature vector to generate dynamic convolutional kernels to transform the RoIs
features to a domain in which the near-zero values represent the noise
information. Then, soft thresholding with channel-wise adaptive thresholds is
applied to convert the near-zero values to zero to filter out noise
information. For the imbalance issue, we propose an IoU-Focal factor (IFF) to
modulate the contributions of the well-regressed boxes and the bad-regressed
boxes to the loss in the training process, paying more attention to the
minority severely overlapping objects. Extensive experiments conducted on
CrowdHuman and CityPersons demonstrate that our methods can help RCNN-based
pedestrian detectors achieve state-of-the-art performance.
