A new infrared small target tracking method is proposed to track objects. First, the two-scale flux density calculating method based on the infrared orientation gradient feature of IR image is presented to extract feature of tracker, which is an effective method to solve the problem in feature extraction of tracking infrared small targets. Second, the least-square trajectory prediction algorithm is applied to deal with the difficulty of target loss in the tracking process. This algorithm makes full use of the continuity and direction of the target motion, avoids the interference of noise and achieves the prediction and discrimination of the target trajectory. The experimental results indicate that the proposed tracking algorithm is superior in precision and has less processing time compared with the contrast algorithms, so it is an efficient method for IR small target tracking in a complex background.
Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.
Deep learning has been widely used in visual tracking due to strong feature extraction ability of convolutional neural network(CNN). Many trackers pre-train CNN primarily and fine-tune it during tracking, which could improve representation ability from off-line database and adjust to appearance variation of the interested object. However, since target information is limited, the network is likely to overfit to a single target state. In this paper, an update strategy composed of two modules is proposed. First, we fine-tune the pre-trained CNN using active learning that emphasizes the most discriminative data iteratively. Second, artificial convolutional features generated from empirical distribution are employed to train fully connected layers, which makes up the deficiency of training examples. Experiments evaluated on VOT2016 benchmark shows that our algorithm outperforms many state-of-the-art trackers.
Robust object tracking is a challenging task in computer vision due to interruptions such as deformation, fast motion and especially, occlusion of tracked object. When occlusions occur, image data will be unreliable and is insufficient for the tracker to depict the object of interest. Therefore, most trackers are prone to fail under occlusion. In this paper, an occlusion judgement and handling method based on segmentation of the target is proposed. If the target is occluded, the speed and direction of it must be different from the objects occluding it. Hence, the value of motion features are emphasized. Considering the efficiency and robustness of Kernelized Correlation Filter Tracking (KCF), it is adopted as a pre-tracker to obtain a predicted position of the target. By analyzing long-term motion cues of objects around this position, the tracked object is labelled. Hence, occlusion could be detected easily. Experimental results suggest that our tracker achieves a favorable performance and effectively handles occlusion and drifting problems.
Video tracking is a main field of computer vision, and TLD algorithm plays a key role in long-term tracking. However, the original TLD ignores the color features of patch in detection, and tracks the common points from grid, then, the tracking accuracy is limited to both of them. This paper presents a novel TLD algorithm with Harris corner and color moment to overcome this drawback. Instead of tracking common points, we screen more important points utilizing Harris corner to reject a half patches, these points are better able to show the object’s textural features. In addition, the color moment classifier replaces patch variance to reduce the errors of detection. The classifier compares mine-dimensional color moment vectors so that it can keep the TLD’s stable speed. Experiment has proved that our TLD tracks a more reliable position and higher ability without affecting the speed.
Visual tracking is important in computer vision. At present, although many algorithms of visual tracking have been proposed, there are still many problems which are needed to be solved, such as occlusion and frame speed. To solve these problems, this paper proposes a novel method which based on compressive tracking. Firstly, we make sure the occlusion happens if the testing result about image features by the classifiers is lower than a threshold value which is certain. Secondly, we mark the occluded image and record the occlusion region. In the next frame, we test both the classifier and the marked image. This algorithm makes sure the tracking is fast, and the result about solving occlusion is much better than other algorithms, especially compressive tracking.
Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.
KEYWORDS: Data compression, Information operations, Networks, Mobile communications, Data communications, Global system for mobile communications, Data acquisition, Data analysis, Lithium, Distance measurement
Collecting reliable and accurate MR data on time plays a vital role in the mobile communication network optimization. However, with the increment of the number of mobile users, network bandwidth cannot meet with mass transfer of MR. A high performance and high compression ratio GSM-MR compression algorithm is proposed to gain better transfer time. This algorithm utilizes two step sorting in order to reduce the distance between similar content, based on the analytic result about similarities of GSM-MR data sorting by different fields. Experimental results reveal that the algorithm does not only decrease compression consuming time, but also ascends compression ratio with the increment of the size of compression data.
Visual tracking is one of the significant research directions in computer vision. Although standard random ferns tracking method obtains a good performance for the random spatial arrangement of binary tests, the effect of the locality of image on ferns description ability are ignored and prevent them to describe the object more accurately and robustly. This paper proposes a novel spatial arrangement of binary tests to divide the bounding box into grids in order to keep more details of the image for visual tracking. Experimental results show that this method can improve tracking accuracy effectively.
JPEG2000 is an important technique for image compression that has been successfully used in many fields. Due to the increasing spatial, spectral and temporal resolution of remotely sensed imagery data sets, fast decompression of remote sensed data is becoming a very important and challenging object. In this paper, we develop an implementation of the JPEG2000 decompression in graphics processing units (GPUs) for fast decoding of codeblock-based parallel compression stream. We use one CUDA block to decode one frame. Tier-2 is still serial decoded while Tier-1 and IDWT are parallel processed. Since our encode stream are block-based parallel which means each block are independent with other blocks, we parallel process each block in T1 with one thread. For IDWT, we use one CUDA block to execute one line and one CUDA thread to process one pixel. We investigate the speedups that can be gained by using the GPUs implementations with regards to the CPUs-based serial implementations. Experimental result reveals that our implementation can achieve significant speedups compared with serial implementations.
Visual tracking is an important task in computer vision. Despite many researches have been done in this area, some problems remain. One of the problems is drifting. To handle the problem, a new appearance model update method based on a forward filtering backward smoothing particle filter is proposed in this paper. A smoothing of previous appearance model is performed by exploiting information of current frame instead of updating instantly in traditional tracking methods. It has been shown that smoothing based on future observations makes previous and current predictions more accurate, thus the appearance model update by our approach is more accurate. And at the same time, online tracking is achieved compared with some previous work in which the smoothing is done in an offline way. With the smoothing procedure, the tracker is more accurate and less likely to drift than traditional ones. Experimental results demonstrate the effectiveness of the proposed method.
Spectral unmixing is an important research hotspot for remote sensing hyperspectral image applications. The unmixing process is comprised of the extraction of spectrally pure signatures (also called endmembers) and the determination of the abundance fractions of endmembers. Due to the inconspicuous signatures of pure spectra and the challenge of inadequate spatial resolution, sparse regression (SR) techniques are adopted in solving the linear spectral unmixing problem. However, the spatial information has not been fully utilized by state-of-art SR-based solutions. In this paper, we propose a new unmixing algorithm which involves in more suitable spatial correlations on sparse unmixing formulation for hyperspectral image. Our algorithm integrates the spectral and spatial information using Adapting Markov Random Fields (AMRF) which is introduced to exploit the spatial-contextual information. Compared with other SR-based linear unmixing methods, the experimental results show that the method proposed in this paper not only improves the characterization of mixed pixels but also obtains better accuracy in hyperspectral image unmixing.
This paper describes a SEU fault injection framework. Based on the assumption of SEU effects and SEU distribution, the quantitative analysis between measured data and simulation model is investigated. By adjusting some parameters in the simulation-based framework, the proposed framework can be very possibly close to the published data and some accelerated radiation experiments. Furthermore, how the JPEG2000 based hardware architecture is sensitive to SEUs can be found out. In terms of hardware resources and operating frequencies, some fault-tolerant techniques can be introduced to the more sensitive parts, which show the framework's effectiveness in fault-tolerant design for image compression applications.
JPEG-LS is an ISO/ITU lossless/near-lossless compression standard for continuous-tone images with both low
complexity and good performance. However, the lack of rate control in JPEG-LS makes it unsuitable for applications,
which have the requirement of the compression to a pre-specified size for purpose of effective storage management or
effective bandwidth management. This paper proposes an efficient rate control scheme for JPEG-LS with a high bitrate.
It is based on a good relationship for the optimal quantization steps of different slices and a good relationship for the
optimal target bitrates of different slices. Comparing with the most previous JPEG-LS with rate control schemes, the
proposed rate control scheme has a uniform performance for the whole image, and it is more suited for the near-lossless
compression, but the most previous JPEG-LS with rate control schemes have the non-uniform performance. The
experimental results show that the proposed rate control scheme achieves better compression performance for remote
sensing compression with a high bitrate.
We propose a zero block detection algorithm and architecture in EBCOT. After the
detailed analysis of wavelet coefficients’ precision and distribution in JPEG2000, there are three
main modes of zero coefficients in the wavelet domain, i.e. zero column, zero stripe and zero code
block. And we also discover that the coding information of each bit plane and the corresponding
passes can be obtained simultaneously in the hardware structure. Therefore, bit plane-parallel and
pass-parallel coding with zero detection is proposed, and its VLSI architecture is shown in details.
The analysis and the corresponding software/hardware experimental results show that the
proposed architecture reduces the processing time greatly compared with others.
The paper presents a context-based arithmetic coder's VLSI architecture used in SPIHT
with reduced memory, which is used for high speed real-time applications. For hardware
implementation, a dedicated context model is proposed for the coder. Each context can be
processed in parallel and high speed operators are used for interval calculations. An embedded
register array is used for cumulative frequency update. As a result, the coder can consume one
symbol at each clock cycle. After FPGA synthesis and simulation, the throughput of our coder is
comparable with those of similar hardware architectures used in ASIC technology. Especially, the
memory capacity of the coder is smaller than those of corresponding systems.
A novel compression algorithm for interferential multispectral images based on adaptive classification and curve-fitting
is proposed. The image is first partitioned adaptively into major-interference region and minor-interference region.
Different approximating functions are then constructed for two kinds of regions respectively. For the major interference
region, some typical interferential curves are selected to predict other curves. These typical curves are then processed by
curve-fitting method. For the minor interference region, the data of each interferential curve are independently
approximated. Finally the approximating errors of two regions are entropy coded. The experimental results show that,
compared with JPEG2000, the proposed algorithm not only decreases the average output bit-rate by about 0.2 bit/pixel
for lossless compression, but also improves the reconstructed images and reduces the spectral distortion greatly,
especially at high bit-rate for lossy compression.
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