# Dynamic Detection and Recognition of Objects Based on Sequential RGB Images
###### Abstract
In the OCIS subtask, the conveyor is the background, and objects on it all belong to one same category named \"Goods\". OCIS is a special task different from semantic segmentation in which segmentation of instances is unnecessary. Because instance segmentation frameworks are designed for multiple categories and are extended from object detection frameworks with a lot of anchors, they often have a deep architecture and suffer from low speed. It hinders them from running on some high cost-effective devices [11]. In this study, a level set map neural network (LSMNet) is proposed for the OCIS subtask. LSMNet is inspired by the level set algorithm, which is a traditional active contour method. In the past three years, two other deep-learning-based active contour methods, level set loss [11] and deep snake [12], have been developed and applied in segmentation tasks. Unlike these methods, which comprise additional active contour constraints on the segmentation losses to improve the quality of masks indirectly, LSMNet utilizes the level set map (LSM) directly. The annotation of instance segmentation is often in the form of a multichannel binary image, in which each channel is the mask of one object. In the proposed method, we use a single level set map (LSM) to annotate the image instead of the multichannel binary image, where each peak of the LSM represents one object. LSMNet adopts a semantic segmentation architecture, for instance, a UNet or a pix2 pix GAN, to predict LSMs. Once the LSM of an image is accurately predicted, the objects can be localized according to the peaks and their areas. An OCIS dataset about on-conveyor objects is established to validate the performance of LSMNet. An experiment on a subset of COCO2017 [13] is also conducted. The results show that LSMNet can achieve an accuracy close to You Only Look At CoefficientTs (YOLACT) [14], which is one of the fastest instance segmentation methods, on big and medium objects with a much higher speed. On the NVIDIA GTX1660 TI GPU, the speed of LSMNet is 80 fps, and that of YOLACT is 25 fps. For the FGR3D subtask, the registered multiview features are treated as the semantic attributes of every category, the features of old on-conveyor objects as the seen categories, and the features of new on-conveyor objects as the unseen categories. A multiview redundancy-free feature network (MVRFFNet) is proposed to learn the map from multiview features to single view on-conveyor features based on the GZSL framework proposed by [PERSON] et al. [3]. Multi-view features of an object are extracted with a pretrained network and combined into a compact registered feature. The registered features are the attributes for training. A Wasserstein generative adversarial network (WGAN) [15] is trained to learn a map function that maps the original retrieve features to a new redundancy-free feature space [1]. The proposed method has two advantages. First, LSMNet realizes instance segmentation with a semantic segmentation network by introducing the innovative LSM annotation. It has a much higher speed than YOLACT and a close performance on objects of big and medium size. Second, the redundancy-free feature is introduced into the multiview framework for fine-grained 3D object recognition. The redundancy-free features generated by MVRFFNet perform much better than the original features in the matching task. This paper is organized as follows. Section 2 reviews the related work. Section 3 proposes the main algorithms. Experiments are presented in Section 4. At last, some conductions are drawn in Section 5.