Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage

Combining Deep Learning and Preference Learning for Object Tracking

Deep CNN Ensemble with Data Augmentation for Object Detection

We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-theart performance on the object detection task. Our method is a variant of the R-CNN model proposed by Girshick et al. [4] with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures ...

Deep learning for class-generic object detection

We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase o...

Deep Reinforcement Learning for Visual Object Tracking in Videos

Convolutional neural network (CNN) models have achieved tremendous success in many visual detection and recognition tasks. Unfortunately, visual tracking, a fundamental computer vision problem, is not handled well using the existing CNN models, because most object trackers implemented with CNN do not effectively leverage temporal and contextual information among consecutive frames. Recurrent ne...

Convolutional Gating Network for Object Tracking

Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem.  The paper presents a new model for combining convolutiona...