DART: Distribution Aware Retinal Transform for Event-based Cameras

We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201

EventCLIP: Adapting CLIP for Event-based Object Recognition

Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP. Due to a shortage of large-scale datasets, training such models for event camera data remains infeasible. Thus, adapting existing models across modalities is an important research challenge. In this work, we introduce EventCLIP, a novel approach that utilizes CLIP for zero-shot and few-shot event-based object recognition. We first generalize CLIP's image encoder to event data by converting raw events to 2D grid-based representations. To further enhance performance, we propose a feature adapter to aggregate temporal information over event frames and refine text embeddings to better align with the visual inputs. We evaluate EventCLIP on N-Caltech, N-Cars, and N-ImageNet datasets, achieving state-of-the-art few-shot performance. When fine-tuned on the entire dataset, our method outperforms all existing event classifiers. Moreover, we explore practical applications of EventCLIP including robust event classification and label-free event recognition, where our approach surpasses previous baselines designed specifically for these tasks.Comment: Better few-shot accuracy. Add results on 1) model fine-tuning 2) compare with concurrent works 3) learning from unlabeled data (unsupervised & semi-supervised

Depth and IMU aided image deblurring based on deep learning

Abstract. With the wide usage and spread of camera phones, it becomes necessary to tackle the problem of the image blur. Embedding a camera in those small devices implies obviously small sensor size compared to sensors in professional cameras such as full-frame Digital Single-Lens Reflex (DSLR) cameras. As a result, this can dramatically affect the collected amount of photons on the image sensor. To overcome this, a long exposure time is needed, but with slight motions that often happen in handheld devices, experiencing image blur is inevitable. Our interest in this thesis is the motion blur that can be caused by the camera motion, scene (objects in the scene) motion, or generally the relative motion between the camera and scene. We use deep neural network (DNN) models in contrary to conventional (non DNN-based) methods which are computationally expensive and time-consuming. The process of deblurring an image is guided by utilizing the scene depth and camera’s inertial measurement unit (IMU) records. One of the challenges of adopting DNN solutions is that a relatively huge amount of data is needed to train the neural network. Moreover, several hyperparameters need to be tuned including the network architecture itself. To train our network, a novel and promising method of synthesizing spatially-variant motion blur is proposed that considers the depth variations in the scene, which showed improvement of results against other methods. In addition to the synthetic dataset generation algorithm, a real blurry and sharp dataset collection setup is designed. This setup can provide thousands of real blurry and sharp images which can be of paramount benefit in DNN training or fine-tuning

Cross View Action Recognition

openCross View Action Recognition (CVAR) appraises a system's ability to recognise actions from viewpoints that are unfamiliar to the system. The state of the art methods that train on large amounts of training data rely on variation in the training data itself to increase their ability to tackle viewpoints changes. Therefore, these methods not only require a large scale dataset of appropriate classes for the application every time they train, but also correspondingly large amount of computation power for the training process leading to high costs, in terms of time, effort, funds and electrical energy. In this thesis, we propose a methodological pipeline that tackles change in viewpoint, training on small datasets and employing sustainable amounts of resources. Our method uses the optical flow input with a stream of a pre-trained model as-is to obtain a feature. Thereafter, this feature is used to train a custom designed classifier that promotes view-invariant properties. Our method only uses video information as input, in contrast to another set of methods that approach CVAR by using depth or pose input at the expense of increased sensor costs. We present a number of comparative analysis that aided the design of the pipelines, farther assessing the power of each component in the pipeline. The technique can also be adopted to existing, trained classifiers, with minimal fine-tuning, as this work demonstrates by comparing classifiers including shallow classifiers, deep pre-trained classifiers and our proposed classifier trained from scratch. Additionally, we present a set of qualitative results that promote our understanding of the relationship between viewpoints in the feature-space.openXXXII CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - InformaticaGoyal, Gaurv

Deep face tracking and parsing in the wild

Face analysis has been a long-standing research direction in the field of computer vision and pattern recognition. A complete face analysis system involves solving several tasks including face detection, face tracking, face parsing, and face recognition. Recently, the performance of methods in all tasks has significantly improved thanks to the employment of Deep Convolutional Neural Networks (DCNNs). However, existing face analysis algorithms mainly focus on solving facial images captured in the constrained laboratory environment, and their performance on real-world images has remained less explored. Compared with the lab environment, the in-the-wild settings involve greater diversity in face sizes, poses, facial expressions, background clutters, lighting conditions and imaging quality. This thesis investigates two fundamental tasks in face analysis under in-the-wild settings: face tracking and face parsing. Both tasks serve as important prerequisites for downstream face analysis applications. However, in-the-wild datasets remain scarce in both fields and models have not been rigorously evaluated in such settings. In this thesis, we aim to bridge that gap of lacking in-the-wild data, evaluate existing methods in these settings, and develop accurate, robust and efficient deep learning-based methods for the two tasks. For face tracking in the wild, we introduce the first in-the-wild face tracking dataset, MobiFace, that consists of 80 videos captured by mobile phones during mobile live-streaming. The environment of the live-streaming performance is fully unconstrained and the interactions between users and mobile phones are natural and spontaneous. Next, we evaluate existing tracking methods, including generic object trackers and dedicated face trackers. The results show that MobiFace represent unique challenges in face tracking in the wild and cannot be readily solved by existing methods. Finally, we present a DCNN-based framework, FT-RCNN, that significantly outperforms other methods in face tracking in the wild. For face parsing in the wild, we introduce the first large-scale in-the-wild face dataset, iBugMask, that contains 21, 866 training images and 1, 000 testing images. Unlike existing datasets, the images in iBugMask are captured in the fully unconstrained environment and are not cropped or preprocessed of any kind. Manually annotated per-pixel labels for eleven facial regions are provided for each target face. Next, we benchmark existing parsing methods and the results show that iBugMask is extremely challenging for all methods. By rigorous benchmarking, we observe that the pre-processing of facial images with bounding boxes in face parsing in the wild introduces bias. When cropping the face with a bounding box, a cropping margin has to be hand-picked. If face alignment is used, fiducial landmarks are required and a predefined alignment template has to be selected. These additional hyper-parameters have to be carefully considered and can have a significant impact on the face parsing performance. To solve this, we propose Region-of-Interest (RoI) Tanh-polar transform that warps the whole image to a fixed-sized representation. Moreover, the RoI Tanh-polar transform is differentiable and allows for rotation equivariance in 1 DCNNs. We show that when coupled with a simple Fully Convolutional Network, our RoI Tanh-polar transformer Network has achieved state-of-the-art results on face parsing in the wild. This thesis contributes towards in-the-wild face tracking and face parsing by providing novel datasets and proposing effective frameworks. Both tasks can benefit real-world downstream applications such as facial age estimation, facial expression recognition and lip-reading. The proposed RoI Tanh-polar transform also provides a new perspective in how to preprocess the face images and make the DCNNs truly end-to-end for real-world face analysis applications.Open Acces

LiDAR-Based Place Recognition For Autonomous Driving: A Survey

LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition, exploring existing challenges, and describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition and for researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table