Search Results for author:
Found 7 papers, 0 papers with code
Improving Multiple Object Tracking With Single Object Tracking
Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance.
High-Performance Discriminative Tracking With Transformers
End-to-end discriminative trackers improve the state of the art significantly, yet the improvement in robustness and efficiency is restricted by the conventional discriminative model, i. e., least-squares based regression.
Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA
To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks.
Fast-deepKCF Without Boundary Effect
Most CF trackers, however, suffer from low frame-per-second (fps) in pursuit of higher localization accuracy by relaxing the boundary effect or exploiting the high-dimensional deep features.
Learning Feature Embeddings for Discriminant Model based Tracking
After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking.
Fast Kernelized Correlation Filters without Boundary Effect
To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter.
On the Relations of Correlation Filter Based Trackers and Struck
In recent years, two types of trackers, namely correlation filter based tracker (CF tracker) and structured output tracker (Struck), have exhibited the state-of-the-art performance.
