Recent efforts have been made on continuously learning concepts over time, i.e., class-incremental learning, which, however, is still an open question to the task of object detection. For the first time, this paper systematically studies the task of learning an end-to-end class-incremental object detection model. Remarkably, this work tailors Faster R-CNN and proposes a Class-Incremental Faster R-CNN (CIFRCN) model that can dynamically add new classes by only using a few labeled images of new objects. Particularly, the domain of "foreground" in a Region Proposal Network is expanded to generate accurate bounding box proposals, and the classifier in Fast R-CNN is enlarged using knowledge distillation to accurately classify the proposals. Extensive experiments on two challenging datasets are conducted to demonstrate the efficacy of the proposed model for adding new objects in a class-incremental way, whereas other detectors quickly fail.

Object detection for automatic visual checkout in self-service vending machines is attracting significant attention in the retail industry. However, several critical challenges have not received enough attention. First, large-scale, high-quality retail image datasets are urgently demanded to train and evaluate the detection models. Second, the trained models should be able to cope with the frequently added new goods at low cost, while most cutting-edge models cannot. In this paper, we propose a new hierarchical large-scale object detection dataset, called Take Goods from Shelves (TGFS), containing 38K images of 24 fine-grained and 3 coarse classes. A preliminary method for solving the goods-adding problem, called Faster R-CNN Class-incremental Object Detector (FCIOD), is also described and evaluated. In addition, several popular methods are benchmarked on the TGFS dataset.

Human detection has received great attention during the past few decades, which is yet still a challenging problem. In this paper, we focus on the problem of 3-D human detection, i.e., finding the human bodies and determining their 3-D coordinates in complex 3-D space using depth data only. Since the traditional sliding-window-based approaches for target localization are time-consuming and the recent deep-learning-based object detectors generate too many region proposals, we propose to utilize the candidate head-top locating stage to efficiently and quickly find the plausible head-top locations. In the second stage, we propose a Depth map, Multiorder depth template, and Height difference map representation encoding three channels of information for each candidate region to utilize the neural network pretrained on large-scale well-annotated datasets to classify the candidate regions. We evaluate our method on four publicly available challenging datasets. Extensive experimental results demonstrate that the proposed method is superior to the state-of-the-art methods while achieving real-time performance.