858 research outputs found
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
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
Early corn stand count of different cropping systems using UAV-imagery and deep learning
Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling areas used, and unable to capture field-scale spatial variability. This study aimed to evaluate the feasibility of an unmanned aerial vehicle (UAV)-based imaging system for estimating early corn stand count in three cropping systems (CS) with different tillage and crop rotation practices. A UAV equipped with an on-board RGB camera was used to collect imagery of corn seedlings (~14 days after planting) of CS, i.e., minimum-till corn-soybean rotation (MTCS), no-till corn-soybean rotation (NTCS), and no-till corn-corn rotation with cover crop implementation (NTCC). An image processing workflow based on a deep learning (DL) model, U-Net, was developed for plant segmentation and stand count estimation. Results showed that the DL model performed best in segmenting seedlings in MTCS, followed by NTCS and NTCC. Similarly, accuracy for stand count estimation was highest in MTCS (R2 = 0.95), followed by NTCS (0.94) and NTCC (0.92). Differences by CS were related to amount and distribution of soil surface residue cover, with increasing residue generally reducing the performance of the proposed method in stand count estimation. Thus, the feasibility of using UAV imagery and DL modeling for estimating early corn stand count is qualified influenced by soil and crop management practices
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