Multiple Object Tracking Based on the Deep Neural Networks and Correlation Filter Online multiobject tracking is a fundamental problem in video analysis based applications. A major challenge in the popular trackingbydetection framework is how to make data association between detections and trajectories. To tackle this, we propose an appearance model and a motion model based on the deep neural networks. In particular, we adopt a deeply learned appearance representation, which is trained on largescale person reidentification datasets using a convolutional neural network, to improve the identification ability of our tracker. Towards the motion model, we design a longshort term memory based network for velocity estimation and use two fullyconnected layers to measure the motion affinity between detections and trajectories. To alleviate the problem introduced by missing detections, a kernelized correlation filter is leveraged to localize the missing objects. Extensive experiments show that the integration of these three components is effective, and our tracker is competitive compared with existing methods.

FANTrack: 3D MultiObject Tracking with Feature Association Network We propose a datadriven approach to online multiobject tracking (MOT) that uses a convolutional neural network (CNN) for data association in a trackingbydetection framework. The problem of multitarget tracking aims to assign noisy detections to apriori unknown and timevarying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at this https URL.