Query: object detection survey

title: A Survey on Performance Metrics for Object Detection Algorithms
abstract: This work explores and compares the plethora of metrics for the performance evaluation of object detection algorithms. Average precision (AP),for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision x recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.

title: Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey
abstract: With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state of the art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state of the art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state of the art methods on the popular public datasets.

title: Deep Domain Adaptive Object Detection: a Survey
abstract: Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state of the art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.

title: A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles
abstract: The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high level module is exploited to build semantic knowledge leveraging the outputs of the low level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

title: Salient Object Detection Techniques in Computer Vision A Survey
abstract: Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning based. In this survey, most of the influential advances in image based SOD from both conventional as well as deep learning based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large scale public datasets. Different metrics considered for assessment of the performance of state of the art salient object detection models are also covered. Some future directions for SOD are presented towards end.

title: Camouflaged Object Detection and Tracking: A Survey
abstract: Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and several essential survey papers exist. However, the research on camouflage object detection and tracking is limited due to its complexity. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. This article reviews the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical perspective. This article also addresses several issues of interest as well as future research direction in this area. We hope this review will help the reader learn the recent advances in camouflaged object detection and tracking.

title: Deep learning based object detection in low altitude UAV datasets: A survey
abstract: Deep learning based object detection solutions emerged from computer vision has captivated full attention in recent years. The growing UAV market trends and interest in potential applications such as surveillance, visual navigation, object detection, and sensors based obstacle avoidance planning have been holding good promises in the area of deep learning. Object detection algorithms implemented in deep learning framework have rapidly became a method for processing of moving images captured from drones. The primary objective of the paper is to provide a comprehensive review of the state of the art deep learning based object detection algorithms and analyze recent contributions of these algorithms to low altitude UAV datasets. The core focus of the studies is low altitude UAV datasets because relatively less contribution was seen in the literature when compared with standard or remote sensing based datasets. The paper discusses the following algorithms: Faster RCNN, Cascade RCNN, R FCN etc. into two stage, YOLO and its variants, SSD, RetinaNet into one stage and CornerNet, Objects as Point etc. under advanced stages in deep learning based detectors. Further, one two and advanced stages of detectors are studied in detail focusing on low altitude UAV datasets. The paper provides a broad summary of low altitude datasets along with their respective literature in detection algorithms for the potential use of researchers. Various research gaps and challenges for object detection and classification in UAV datasets that need to deal with for improving the performance are also listed.

title: A Comprehensive Survey on Computer Vision Based Approaches for Moving Object Detection
abstract: Moving object detection has achieved a noticeable attention in many computer vision applications. The research community have contributed lot of works for dealing with major challenges of moving object detection in real world scenarios. The paper presents a comprehensive review on different moving object detection techniques classified into four categories:  Modeling Based techniques; Frame Difference Based techniques; Optical Flow Based techniques and Deep Learning Based techniques. Moreover, detailed descriptions of various methods in each of this category are also provided.

title: A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning
abstract: An accurate and effective perception of environment is important for autonomous vehicle and robot. The perception system needs to obtain the 3D information of objects, which includes objects' space location and pose. Camera is widely equipped on autonomous vehicle because of its price advantage. However, the monocular camera cannot provide depth information which is necessary for 3D object detection. Many algorithms based on monocular 3D object detection have been developed in recent years. Deep learning is popular for perception system which transforms image data from camera into semantic information. This paper presents an overview of monocular 3D object detection algorithms based on deep Learning and summarize the contributions and limitations of these algorithms. We also compare the performance of different algorithms on different datasets.

title: Feature Fusion Methods in Deep Learning Generic Object Detection: A Survey
abstract: Feature fusion has become one of the most popular orientations in object detection, which has been widely applied to enrich object representation, especially for the small objects. However, there has not been published an integrated survey paper that concentrate on the feature fusion methods in deep learning object detection. Therefore, we would like to sort out the relevant content. And we believe that a comprehensive survey is necessary and can provide some useful guidance for the follow up work. In this paper, we first introduce some classical backbone networks which adopt feature fusion methods. Then we analyses the fusion techniques of several typical or state of the art frameworks. Thirdly, we present a synthesize survey of fusion strategies. At last, the future development trends and challenges are summarized. This survey infers that feature fusion methods have acquired some good results especially in recent five years, but further improvements or potential research directions still widely exist.