Deformable Convolutional Networks Tracker

Object tracking is a fundamental topic in computer vision. It is an interdisciplinary scientific filed involving machine learning, pattern recognition etc, and has a wide applicability. Although, convolutional neural networks (CNNs) have achieved significant success for visual recognition tasks. Deformation and scale variation of targets are huge challenges in object tracking. They cannot address these challenges for relying on massive amounts of data to build deep models. They are inherently limited to model geometric transformations due to the fixed geometry in the building blocks. In short, CNNs are inherently limited to model large and unknown transformation. In this paper, we introduced a deformable convolution module to construct a novel deformable convolutional networks tracker(DCT). The tracker illustrates outstanding performance in the Visual Tracker Benchmark (OTB)100[7] benchmark with scale variation and deformation attributes. Introduction Computer vision is an interdisciplinary field that deals with how computers can obtain high-level understanding information from images or videos. It can be seen as an extension of human vision and perception. In the past two decades, computer vision technology are widely used in intelligent control, decision systems, information retrieval, human-computer interaction, etc. In computer vision, object tracking (also known as visual tracking) has always been an important topic. Visual tracking determines the motion information, direction and trajectory of the target by estimating the position, shape or occupied area of the tracking target in a sequence of continuous video images, and realizes the analysis and understanding of the behavior of the moving target in order to complete a higher level task. Visual tracking has important research significance and broad application prospects in both military defense and civil security. Specific research and applications include: intelligent video surveillance, modern military, video-based human-computer interaction, smart transportation system, intelligent visual navigation, 3D reconstruction and so on. The visual tracking framework includes four parts[4]: motion model, feature extractor, observation model, model updater. A key challenge in the visual object tracking is how to accommodate geometric viations or model transforms in object scale, viewpoint, and part deformation. In general, there are two methods. The first is to build a training data set with enough changes needed, which increases existing data samples. The second is to use transform invariant features and algorithms. This category contains many well-known techniques such as Scale Invariant Feature Transform(SIFT) [2] and sliding window based object detection paradigms. The above approach has two drawbacks. First, assume that the geometric transformation is fixed and known. This assumption prevents the promotion of new tasks with unknown geometric transformations that are not properly modeled. Second, hand-crafted invariant features and algorithm designs can be difficult or infeasible for overly complex transformations, even if they are known. In recent years, (CNNs) [3] has achieved great success in the field of target tracking with its excellent feature modeling capabilities. In the basic test database such as OTB, Visual object tracking(VOT), the top algorithms of accuracy are based on the visual tracking algorithm of CNNs. However, they still have the above two drawbacks. Their ability to model geometric transformations comes primarily from extensive data enhancement, large model capacity and some simple manual modules. CNNs are inherently limited to simulating large 231 unknown transforms. This limitation stems from the fixed geometry of the CNN module: the convolution unit samples the input feature map at a fixed location. In this work, we introduced a new module deformable convolution that greatly enhances CNN's ability to model geometric transformations. It adds the 2D offsets to the regular grid sampling position in the standard convolution.