Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, both spatial and appearance information will become ambiguous simultaneously due to the high overlap between objects. In this paper, we demonstrate that this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence state and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Furthermore, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, by leveraging both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and occlusion are frequent and severe. The code and models are available at https://github.com/ymzis69/HybirdSORT

LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion

Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance segmentation in a unified framework. To improve the performance of LiDAR-based real-time panoptic segmentation, this study proposes a spatiotemporal sequential data fusion strategy that fused points in “thing classes” based on accurate data statistics. The data fusion strategy could increase the proportion of valuable data in unbalanced datasets, and thus managed to mitigate the adverse impact of class imbalance in the limited training data. Subsequently, by improving the codec network, the multiscale features shared by semantic and instance branches were efficiently aggregated to achieve accurate panoptic segmentation for each LiDAR scan. Experiments on the publicly available dataset SemanticKITTI showed that our approach could achieve an effective balance between accuracy and efficiency, and it was also applicable to other point cloud segmentation tasks

Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, spatial and appearance information will become ambiguous simultaneously due to the high overlap among objects. In this paper, we demonstrate this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, with both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and severe occlusion frequently happen with complex motions. The code and models are available at https://github.com/ymzis69/HybridSORT

Circadian humidity fluctuation induced capillary flow for sustainable mobile energy

Circadian humidity fluctuation is an important factor that affects human life all over the world. Here we show that spherical cap-shaped ionic liquid drops sitting on nanowire array are able to continuously output electricity when exposed to outdoor air, which we attribute to the daily humidity fluctuation induced directional capillary flow. Specifically, ionic liquid drops could absorb/desorb water around the liquid/vapor interface and swell/shrink depending on air humidity fluctuation. While pinning of the drop by nanowire array suppresses advancing/receding of triple-phase contact line. To maintain the surface tension-regulated spherical cap profile, inward/outward flow arises for removing excess fluid from the edge or filling the perimeter with fluid from center. This moisture absorption/desorption-caused capillary flow is confirmed by in-situ microscope imaging. We conduct further research to reveal how environmental humidity affects flow rate and power generation performance. To further illustrate feasibility of our strategy, we combine the generators to light up a red diode and LCD screen. All these results present the great potential of tiny humidity fluctuation as an easily accessible anytime-and-anywhere small-scale green energy resource. Droplet generators convert mechanical movements of droplets into small-scale electricity. Here, Tang et al. report a humidity-driven power generator by utilizing daily humidity fluctuation in atmosphere enabling continuous generation of electricity upon moisture absorption and desorption cycles

Continuous Energy Harvesting from Ubiquitous Humidity Gradients using Liquid-Infused Nanofluidics

Humidity-based power generation that converts internal energy of water molecules into electricity is an emerging approach for harvesting clean energy from nature. Here it is proposed that intrinsic gradient within a humidity field near sweating surfaces, such as rivers, soil, or animal skin, is a promising power resource when integrated with liquid-infused nanofluidics. Specifically, capillary-stabilized ionic liquid (IL, Omim(+)Cl(-)) film is exposed to the above humidity field to create a sustained transmembrane water-content difference, which enables asymmetric ion-diffusion across the nanoconfined fluidics, facilitating long-term electricity generation with the power density of approximate to 12.11 mu W cm(-2). This high record is attributed to the nanoconfined IL that integrates van der Waals and electrostatic interactions to block movement of Omim(+) clusters while allowing for directional diffusion of moisture-liberated Cl+. This humidity gradient triggers large ion-diffusion flux for power generation indicates great potential of sweating surfaces considering that most of the earth is covered by water or soil