452 research outputs found
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985
RGB-T salient object detection via fusing multi-level CNN features
RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast
RGB-T Tracking Based on Mixed Attention
RGB-T tracking involves the use of images from both visible and thermal
modalities. The primary objective is to adaptively leverage the relatively
dominant modality in varying conditions to achieve more robust tracking
compared to single-modality tracking. An RGB-T tracker based on mixed attention
mechanism to achieve complementary fusion of modalities (referred to as MACFT)
is proposed in this paper. In the feature extraction stage, we utilize
different transformer backbone branches to extract specific and shared
information from different modalities. By performing mixed attention operations
in the backbone to enable information interaction and self-enhancement between
the template and search images, it constructs a robust feature representation
that better understands the high-level semantic features of the target. Then,
in the feature fusion stage, a modality-adaptive fusion is achieved through a
mixed attention-based modality fusion network, which suppresses the low-quality
modality noise while enhancing the information of the dominant modality.
Evaluation on multiple RGB-T public datasets demonstrates that our proposed
tracker outperforms other RGB-T trackers on general evaluation metrics while
also being able to adapt to longterm tracking scenarios.Comment: 14 pages, 10 figure
Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients
The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing
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