ANMS: Asynchronous Non-Maximum Suppression in Event Stream

The non-maximum suppression (NMS) is widely used in frame-based tasks as an essential post-processing algorithm. However, event-based NMS either has high computational complexity or leads to frequent discontinuities. As a result, the performance of event-based corner detectors is limited. This paper proposes a general-purpose asynchronous non-maximum suppression pipeline (ANMS), and applies it to corner event detection. The proposed pipeline extract fine feature stream from the output of original detectors and adapts to the speed of motion. The ANMS runs directly on the asynchronous event stream with extremely low latency, which hardly affects the speed of original detectors. Additionally, we evaluate the DAVIS-based ground-truth labeling method to fill the gap between frame and event. Evaluation on public dataset indicates that the proposed ANMS pipeline significantly improves the performance of three classical asynchronous detectors with negligible latency. More importantly, the proposed ANMS framework is a natural extension of NMS, which is applicable to other asynchronous scoring tasks for event cameras.

Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams

Event cameras are neuromorphic vision sensors representing visual information as sparse and asynchronous event streams. Most state-of-the-art event-based methods project events into dense frames and process them with conventional learning models. However, these approaches sacrifice the sparsity and high temporal resolution of event data, resulting in a large model size and high computational complexity. To fit the sparse nature of events and sufficiently explore their implicit relationship, we develop a novel attention-aware framework named Event Voxel Set Transformer (EVSTr) for spatiotemporal representation learning on event streams. It first converts the event stream into a voxel set and then hierarchically aggregates voxel features to obtain robust representations. The core of EVSTr is an event voxel transformer encoder to extract discriminative spatiotemporal features, which consists of two well-designed components, including a multi-scale neighbor embedding layer (MNEL) for local information aggregation and a voxel self-attention layer (VSAL) for global representation modeling. Enabling the framework to incorporate a long-term temporal structure, we introduce a segmental consensus strategy for modeling motion patterns over a sequence of segmented voxel sets. We evaluate the proposed framework on two event-based tasks: object classification and action recognition. Comprehensive experiments show that EVSTr achieves state-of-the-art performance while maintaining low model complexity. Additionally, we present a new dataset (NeuroHAR) recorded in challenging visual scenarios to address the lack of real-world event-based datasets for action recognition.

A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition

It is a popular solution to convert events into dense frame-based representations to use the well-pretrained CNNs in hand. Although with appealing performance, this line of work sacrifices the sparsity/temporal precision of events and usually necessitates heavy-weight models, thereby largely weakening the advantages and real-life application potential of event cameras. A more application-friendly way is to design deep graph models for learning sparse point-based representations from events. Yet, the efficacy of these graph models is far behind the frame-based counterpart with two key limitations: ($i$) simple graph construction strategies without carefully integrating the variant attributes (i.e., semantics, spatial and temporal coordinates) for each vertex, leading to biased graph representation; ($ii$) deficient learning because the lack of well pretraining models available. Here we solve the first problem by introducing a new event-based graph CNN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To alleviate the learning difficulty, we propose to leverage the dense representation counterpart of events as a cross-representation auxiliary to supply additional supervision and prior knowledge for the event graph. To this end, we form a frame-to-graph transfer learning framework with a customized hybrid distillation loss to well respect the varying cross-representation gaps across layers. Extensive experiments on multiple vision tasks validate the effectiveness and high generalization ability of our proposed model and distillation strategy (Core components of our codes are submitted with supplementary material and will be made publicly available upon acceptance)

Observability Analysis of Graph SLAM-Based Joint Calibration of Multiple Microphone Arrays and Sound Source Localization

Multiple microphone arrays have many applications in robot audition, including sound source localization, audio scene perception and analysis, etc. However, accurate calibration of multiple microphone arrays remains a challenge because there are many unknown parameters to be identified, including the Euler angles, geometry, asynchronous factors between the microphone arrays. This paper is concerned with joint calibration of multiple microphone arrays and sound source localization using graph simultaneous localization and mapping (SLAM). By using a Fisher information matrix (FIM) approach, we focus on the observability analysis of the graph SLAM framework for the above-mentioned calibration problem. We thoroughly investigate the identifiability of the unknown parameters, including the Euler angles, geometry, asynchronous effects between the microphone arrays, and the sound source locations. We establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. These conditions are closely related to the variation in the motion of the sound source and the configuration of microphone arrays, and have intuitive and physical interpretations. We also discover several scenarios where the unknown parameters are not uniquely identifiable. All theoretical findings are demonstrated using simulation data.

A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research.

EV-VGCNN: A Voxel Graph CNN for Event-based Object Classification

Event cameras report sparse intensity changes and hold noticeable advantages of low power consumption, high dynamic range, and high response speed for visual perception and understanding on portable devices. Event-based learning methods have recently achieved massive success on object recognition by integrating events into dense frame-based representations to apply traditional 2D learning algorithms. However, these approaches introduce much redundant information during the sparse-to-dense conversion and necessitate models with heavy-weight and large capacities, limiting the potential of event cameras on real-life applications. To address the core problem of balancing accuracy and model complexity for event-based classification models, we (1) construct graph representations for event data to utilize their sparsity nature better and design a lightweight end-to-end graph neural network (EV-VGCNN) for classification; (2) use voxel-wise vertices rather than traditional point-wise methods to incorporate the information from more points; (3) introduce a multi-scale feature relational layer (MFRL) to extract semantic and motion cues from each vertex adaptively concerning its distances to neighbors. Comprehensive experiments show that our approach advances state-of-the-art classification accuracy while achieving nearly 20 times parameter reduction (merely 0.84M parameters).

CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse

The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which the well-learned source modality provides supervisory signals to facilitate the learning process for the new modality. To better extract the complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal interactions and cross-level transmissions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in zero-shot saliency detection and pre-training on a new modality, as well as the advantages in selecting and fusing cross-modal/cross-level complements.

BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion

In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases. However, the recently introduced notion of Pre-mappability (PreM) has shown that, in key applications of interest, aggregates can be used in recursion to optimize the perfect-model semantics of aggregate-stratified programs. Therefore we can preserve the declarative formal semantics of such programs while achieving a highly efficient operational semantics that is conducive to scalable implementations on parallel and distributed platforms. In this paper, we show that with PreM, a wide spectrum of classical algorithms of practical interest, ranging from graph analytics and dynamic programming based optimization problems to data mining and machine learning applications can be concisely expressed in declarative languages by using aggregates in recursion. Our examples are also used to show that PreM can be checked using simple techniques and templatized verification strategies. A wide range of advanced BigData applications can now be expressed declaratively in logic-based languages, including Datalog, Prolog, and even SQL, while enabling their execution with superior performance and scalability.

RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition

Motion behaviors of a rigid body can be characterized by a 6-dimensional motion trajectory, which contains position vectors of a reference point on the rigid body and rotations of this rigid body over time. This paper devises a Rotation and Relative Velocity (RRV) descriptor by exploring the local translational and rotational invariants of motion trajectories of rigid bodies, which is insensitive to noise, invariant to rigid transformation and scaling. A flexible metric is also introduced to measure the distance between two RRV descriptors. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To illustrate the descriptive ability of the RRV descriptor, we explore it for different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that this simple RRV descriptor outperforms the previous ones regarding recognition accuracy without increasing computational cost.