Gait phase segmentation, pivotal for understanding lower limb motion, finds applications in diverse fields like medicine and sports. While existing method often struggle with accuracy and adaptability in real-world settings, this study presents a novel methodology employing particle filters for precise lower limb motion capture (MoCap) utilizing inertial sensors, which can be used in more everyday environments and in a wider range of applications over a longer period of time. The innovative approach adeptly tracks walking movements, labeling six gait phases via skeleton reconstruction facilitated by the MoCap algorithm. Subsequently, we propose a neural network architecture amalgamating temporal convolutional network (TCN), graph convolutional network (GCN), and long short-term memory (LSTM). This architecture integrates raw data from inertial sensors with joint angles derived from reconstructed motion, achieving accurate segmentation of the six gait phases. Experimental validation compares the MoCap algorithm against an optical motion capture system, and the neural network’s performance against state-of-the-art methods. Results demonstrate our method’s superior accuracy of 96.94%, highlighting its efficacy in addressing gait phase segmentation challenges and propelling advancements in gait analysis.

In recent years, the rapid advancement of image generation techniques has resulted in the widespread abuse of manipulated images, leading to a crisis of trust and affecting social equity. Thus, the goal of our work is to detect and localize tampered regions in images. Many deep learning based approaches have been proposed to address this problem, but they can hardly handle the tampered regions that are manually fine-tuned to blend into image background. By observing that the boundaries of tempered regions are critical to separating tampered and non-tampered parts, we present a novel boundary-guided approach to image manipulation detection, which introduces an inherent bias towards exploiting the boundary information of tampered regions. Our model follows an encoder-decoder architecture, with multi-scale localization mask prediction, and is guided to utilize the prior boundary knowledge through an attention mechanism and contrastive learning. In particular, our model is unique in that 1) we propose a boundary-aware attention module in the network decoder, which predicts the boundary of tampered regions and thus uses it as crucial contextual cues to facilitate the localization; and 2) we propose a multi-scale contrastive learning scheme with a novel boundary-guided sampling strategy, leading to more discriminative localization features. Our state-of-art performance on several public benchmarks demonstrates the superiority of our model over prior works.

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca.