Detection in Crowded Scenes: One Proposal, Multiple Predictions

Chu, Xuangeng; Zheng, Anlin; Zhang, Xiangyu; Sun, Jian

doi:10.48550/arxiv.2003.09163

Cited by 2 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rukhovich et al [18] perform a detection in several iterations by feeding the model with information from the previous iterations. Chu et al [19] make multiple detections from a single proposed region. Xie et al [20] suggest replacing traditional rectangular anchors with oriented anchors.…”

Section: Anchor-based Approachesmentioning

confidence: 99%

An efficient anchor‐free method for pig detection

Mattina

Benzinou

Nasreddine

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Given the rapid growth of commercial pig farms, the need to automatically monitor pig behaviour becomes more important in order to assist farmers. Recent advances in convolutional neural networks may pave the way for new solutions. However, the primary task of individual pig detection under real‐world conditions is still a challenging task. Previous studies used anchor‐based frameworks that are unsuitable for such crowded scenarios with extreme overlapping. Furthermore, most applications focus on specific levels of brightness, farm facilities, or pig species without considering generalization. To tackle these problems, an anchor‐free pig detection method based on pig centre localization is first proposed. Then, a novel negative training data augmentation technique is introduced using examples from outside the training distribution. Furthermore, using the test time augmentation technique is proposed to improve the model performance. Experiments are conducted on two online pig detection datasets; the network surpasses state‐of‐the‐art results for both datasets. It is also found that the proposed method outperforms the latest anchor‐free techniques commonly used in crowded scenarios. The method can detect pigs individually, even if their bounding boxes overlap strongly or occlude each other. Moreover, the real‐time system achieves an improvement of 10% in Fmeasure$F_{\text{measure}}$ when testing in unconstrained real‐world conditions.

show abstract

Section: Anchor-based Approachesmentioning

confidence: 99%

An efficient anchor‐free method for pig detection

Mattina

Benzinou

Nasreddine

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Xiao¹,

Xu²,

Zhong³

et al. 2020

Preprint

View full text Add to dashboard Cite

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by what the occlusion is given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability.

show abstract

Detection in Crowded Scenes: One Proposal, Multiple Predictions

Cited by 2 publications

References 41 publications

An efficient anchor‐free method for pig detection

An efficient anchor‐free method for pig detection

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Contact Info

Product

Resources

About