The inconsistency between classification and regression is a common problem in the field of object detection. Such may lead to undetected objects, false detection, boxes overlapping detection results. It has been determined that mainly caused by feature coupling lack information regarding interactions heads. In this study, characteristics spatial invariance were used, ability fit data distribut...

There have been significant advances in 3D object detection using LiDAR and camera fusion for autonomous driving. However, it is surprisingly difficult to effectively design location strategies point cloud-based networks. In this paper, we propose a novel two-stage sequential (TSF) method. the first stage of fusion, TSF generates enhanced cloud by combining raw semantic information image instan...

In this article, we propose a novel and highly practical score-level fusion approach called dynamic belief (DBF) that directly integrates inference scores of individual detections from multiple object detection methods. To effectively integrate the outputs detectors, level ambiguity in each score is estimated using confidence model built on precision-recall relationship corresponding detector. ...

The object tracking algorithm based on Siamese network often extracts the deep feature of target to be tracked from first frame video sequence as a template, and uses template for whole process. Because manually annotated in is more accurate, these algorithms have stable performance. However, it difficult adapt changing features only using extracted frame. Inspired by fusion transformer, this p...

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and large variation appearances, e.g., size shape. To address these challenges, we propose novel Context-aware Cross-level Fusion Network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" ...

This paper presents the visual surveillance aspects of a distributed intelligent system that has been developed in the context of aircraft activity monitoring. The overall tracking system comprises three main modules — Motion Detection, Object Tracking and Data Fusion. In this paper we primarily focus on the object tracking and data fusion modules.

In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved. One of them is how to fuse a series of images that might differ in their viewpoints. In this paper we present a well-founded approach for the fusion of...

We propose a new method for fusing a LIDAR point cloud and camera-captured images in the deep convolutional neural network (CNN). The proposed method constructs a new layer called non-homogeneous pooling layer to transform features between bird view map and front view map. The sparse LIDAR point cloud is used to construct the mapping between the two maps. The pooling layer allows efficient fusi...

This paper presents novel algorithms exploiting multiple camera inputs and segmentation techniques, which can be applied to image fusion, disparity detection and object extraction. Differently focused images, stereo pairs and both of them are used for fusion, disparity detection and object extraction, respectively. Firstly, image fusion is done by segmention of each image and determination of f...