Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter

Abstract

Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet).

Automatic Object Tracking and Segmentation Using Unsupervised SiamMask

“…Tracking algorithms still face a number of challenges like distortion and deformation, changes in lighting, motion blur, clutter and occlusion to name a few [22]. Moreover, for practical applications, it is important that the tracking is not only accurate, but also real-time [23]. The recent trackers based on deep features achieve a higher accuracy, however they are extremely slow resulting in frames being dropped when put in real-time conditions.…”

“…Hence, work is being done to increase accuracy while maintaining speed. Shin et al [23] proposed a modified kernalized correlation filter (KCF) that identifies tracking failure and tries to resume tracking by searching over multiple windows. In another work, Zhang et al [24] attempted to combine multi-object detection and re-identification in a single network to increase speed.…”

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

“…Zhang et al [39] proposed an improved STC algorithm by incorporating color naming and histogram of oriented gradients features in the STC framework, along with improved scale strategy and adaptive model update scheme. Shin et al [40] proposed an improved KCF-based tracking algorithm. They incorporated module for detection of tracking failure, mechanism for re-tracking in multiple search windows and analysis of motion vectors for deciding the search window in the KCF framework.…”

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

“…Correlation filters have been broadly applied in object tracking [ 24 , 25 , 26 , 27 , 28 ]. To solve scale estimation in correlation filtering, Danelljan et al [ 29 ] proposed a tracker based on correlation filters for translation and scale in image scale pyramid representation.…”

“…This method can effectively prevent the tracking model from the wrong appearance. Experimental results have been presented on de facto standard videos to show the efficacy of the proposed method over STC [ 19 ], DCF CA [ 26 ], Modified KCF [ 28 ], MACF [ 31 ], Modified STC [ 40 ], and AFAM-PEC [ 41 ]. …”