MSSTResNet TLD: A robust tracking method based on tracking learning detection framework by using multi scale spatio temporal residual network feature model
Abstract The performance of tracking task is directly dependent on the appearance features of target object, a robust approach for constructing appearance features is crucial for adaptation the appearance change. To construct an accurate and robust appearance model for visual object tracking, we modify original deep residual learning network architecture and name it Multi Scale Residual Network (MSResNet) The first video frame image and its related information of the current input video sequence are used to learn a multi scale appearance model of target object and a loss function is minimized over the appearance features. Meanwhile, spatial information of each video frame and temporal information between successive video frames effectively combine with MSResNet. And thus the features are generated by Multi Scale Spatio Temporal Residual Network, which is named MSSTResNet feature model, can adapt to scale variation, illumination variation, background clutters, severe deformation of the target object, and so on. We implement a robust tracking method based on tracking learning detection framework by using our proposed MSSTResNet feature model and name it MSSTResNet TLD tracker. Unlike the previous tracking methods, the MSResNet architecture is not offline pre trained on a large auxiliary datasets but is directly learned end to end with a multi task loss by using the current input video sequence. Furthermore, the multi task loss function utilizes the classification loss and regression loss that is more accurate for target localization. Our experimental results demonstrate that the proposed tracking method outperforms the current state of the art tracking methods on Visual Object Tracking Benchmark (VOT 2016) Object Tracking Benchmark (OTB 2015) and Unmanned Aerial Vehicles (UAV20L) test datasets. Furthermore, our MSSTResNet TLD tracker is faster than previous most trackers based on deep Convolutional Neural Network (ConvNet or CNN) and our tracker is extremely robust to the tiny target object. Our source code is available for download at https:/github.com/binger1225/MSSTResNet TLD Tracker
Neurocomputing