Visual tracking with online assessment and improved sampling strategy

The kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets

Novel Aggregated Solutions for Robust Visual Tracking in Traffic Scenarios

This work proposes novel approaches for object tracking in challenging scenarios like severe occlusion, deteriorated vision and long range multi-object reidentification. All these solutions are only based on image sequence captured by a monocular camera and do not require additional sensors. Experiments on standard benchmarks demonstrate an improved state-of-the-art performance of these approaches. Since all the presented approaches are smartly designed, they can run at a real-time speed

Diskriminativni korelacijski filter s segmentacijo in uporabo konteksta za robustno sledenje

Visual object tracking is an area in the field of computer vision, which has seen great popularity increase due to a large availability of video data. There are many different tracking tasks, such as multiple object tracking, long-term tracking and specialized trackers, expected to perform well in a very specific domain. In this work, we focus on online generic short-term single object tracking, which can be considered the base visual tracking task and can be adaptable to any of the previously mentioned tasks. We propose a new tracker, based on correlation filtering, augmented with context information and a predicted object segmentation mask. The results on benchmarks fall far behind the current state-of-the-art, however the proposed method consistently outperforms baseline trackers, which shows the methods potential for future improvements.Vizualno sledenje objektom je področje računalniškega vida, ki je v zadnjih letih doživelo velik razcvet, zahvaljujoč dostopnosti video vsebin. Problem lahko razdelimo na več podnalog, na primer sledenje več objektom, dolgoročno sledenje ali specializirano sledenje za točno določeno domeno. V tem delu se omejimo na splošne kratkoročne sledilnike, ki sledijo enemu objektu. To lahko namreč razumemo kot najbolj osnovno nalogo vizualnega sledenja, ki jo lahko razširimo za delovanje na prej omenjenih problemih. V delu predstavimo nov sledilnik, ki temelji na sledenju s korelacijskimi filtri, razširimo pa ga z uporabo kontekstne informacije in segmentacijske maske. V primerjavi z ostalimi sledilniki predlagana metoda sicer ne dosega rezultatov, primerljivih z najmodernejšimi sledilniki, vendar pa dosledno dosega boljše rezultate od osnovnejših sledilnikov, kar kaže na potencial metode za nadaljnje izboljšave