… We also present our own single-image object detection method that uses large synthetic
datasets for training. Using a distributed, parallel learning algorithm, we train from very large …

… , lenses, resolutions and frame rates producing different image qualities. A low quality image
sequence can confuse moving object detection algorithms if they are not well designed and …

… is marked by the white box in the 2D image. The important region indicated by the attention
… head is a discriminative part of a human from camera image (it is difficult to recognize a head …

… Finally, to handle objects that are difficult to detect in point clouds, we introduce an image-…
camera 3D object detection. As shown in Fig. 2, given a LiDAR BEV feature map and an image …

… detect objects in the camera's image stream combined with the UAV's navigation data, the
onboard computer is able to georeference each object detection to … utilizes an object's position …

… 3D Detection Using Multi-modal Fusion We focus on camera-… object detector are a set of
2D bounding boxes in the image … For k 2D detection candidates in one image can be defined …

… plane and onto a perpendicular image plane, and fuses both representations with the camera
image in a neural network for object detection. [16] fuses lidar and camera data in a neural …

… indicate position of each object in image. Objects can be … object detection using image
segmentation [7], Viola-Jones algorithm for object detection [2], feature-based object detection [8] …

… In this paper a deep neural network is used to do 3D object detection on a radar
pointcloud and a camera image. The network is similar to the network used by Ku et al. [11]. …

… from stereo vision camera, rectified image frames are taken and 3D cloud is formed via
disparity calculation [3]. Pertained CNN (MobileNetSSD) [7] is used for object detection and …