2,857 research outputs found
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation
Aerial tracking, which has exhibited its omnipresent dedication and splendid
performance, is one of the most active applications in the remote sensing
field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system,
equipped with a visual tracking approach, has been widely used in aviation,
navigation, agriculture,transportation, and public security, etc. As is
mentioned above, the UAV-based aerial tracking platform has been gradually
developed from research to practical application stage, reaching one of the
main aerial remote sensing technologies in the future. However, due to the
real-world onerous situations, e.g., harsh external challenges, the vibration
of the UAV mechanical structure (especially under strong wind conditions), the
maneuvering flight in complex environment, and the limited computation
resources onboard, accuracy, robustness, and high efficiency are all crucial
for the onboard tracking methods. Recently, the discriminative correlation
filter (DCF)-based trackers have stood out for their high computational
efficiency and appealing robustness on a single CPU, and have flourished in the
UAV visual tracking community. In this work, the basic framework of the
DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art
DCF-based trackers are orderly summarized according to their innovations for
solving various issues. Besides, exhaustive and quantitative experiments have
been extended on various prevailing UAV tracking benchmarks, i.e., UAV123,
UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903
frames in total. The experiments show the performance, verify the feasibility,
and demonstrate the current challenges of DCF-based trackers onboard UAV
tracking.Comment: 28 pages, 10 figures, submitted to GRS
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal information degrades the tracking performance during challenges such
as partial occlusion and deformation. In this work, we focus on making use of
the rich flow information in consecutive frames to improve the feature
representation and the tracking accuracy. Firstly, individual components,
including optical flow estimation, feature extraction, aggregation and
correlation filter tracking are formulated as special layers in network. To the
best of our knowledge, this is the first work to jointly train flow and
tracking task in a deep learning framework. Then the historical feature maps at
predefined intervals are warped and aggregated with current ones by the guiding
of flow. For adaptive aggregation, we propose a novel spatial-temporal
attention mechanism. Extensive experiments are performed on four challenging
tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed
method achieves superior results on these benchmarks.Comment: Accepted in CVPR 201
- …