Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion

Fast Real-time Video Object Segmentation with Tangled Memory Network

In this paper, we present a fast real-time Tangled Memory Network (TMN) that segments the objects effectively and efficiently for semi-supervised video object segmentation (VOS). We propose a tangled reference encoder and a memory bank organization mechanism based on a state estimator to fully utilize the mask features and alleviate memory overhead and computational burden brought by the unlimited memory bank used in many memory-based methods. First, TMN exploits the mask features that uncover abundant object information like edges and contours but are not fully explored in existing methods. Specifically, a tangled two-stream reference encoder is designed to extract and fuse the features from both RGB frames and the predicted masks. Second, to indicate the quality of the predicted mask and feedback the online prediction state for organizing the memory bank, we devise a target state estimator to learn the IoU score between the predicted mask and groundtruth. Moreover, to accelerate the forward process and avoid memory overflow, we use a memory bank of fixed size to store historical features by designing a new efficient memory bank organization mechanism based on the mask state score provided by the state estimator. We conduct comprehensive experiments on public benchmarks DAVIS and YouTube-VOS, demonstrating that our method obtains competitive results while running at high speed (66 FPS on DAVIS16-val set).

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems

Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle's sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor's image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception.