Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene

Image quality assessment of fast fourier transform domain watermarked images

Digital watermarking is the processing of embedding digital signature into the host media such as image, video, text, audio etc. During the watermarking process, images are subjected to variety of attacks such as noise in transmission channel, geometric attacks,compression, processing like filtering, etc, all this affect the visual quality of watermarked image. Thus, there is a need for image quality assessment of watermarked images in relation to the original images. Several measures of image metrics are available in the field of image processing however they are application based. This paper discusses watermarking in FFT domain and some of the image quality metric that can be applied. Experiments are conducted using the Full Reference (FR) images. We used Mean Square Error (MSE), Root Mean Square (RMS), Structural Similarity (SSIM), Image Fidelity Measure (IFM), Correlation Coefficient Index (CCI) and Peak Signal to Noise Ratio (PSNR) as our quality assessment. Result shows that CCI, SSIM, and IFM are most appropriate for measuring quality of watermarking system

Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality

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)