Color Histogram Similarity Research Articles

Multi-Step Prediction of Typhoon Tracks Combining Reanalysis Image Fusion Using Laplacian Pyramid and Discrete Wavelet Transform with ConvLSTM

Typhoons often cause huge losses, so it is significant to accurately predict typhoon tracks. Nowadays, researchers predict typhoon tracks with the single step, while the correlation of adjacent moments data is small in long-term prediction, due to the large step of time. Moreover, recursive multi-step prediction results in the accumulated error. Therefore, this paper proposes to fuse reanalysis images at the similarly historical moment and predicted images through Laplacian Pyramid and Discrete Wavelet Transform to reduce the accumulated error. That moment is determined according to the difference in the moving angle at predicted and historical moments, the color histogram similarity between predicted images and reanalysis images at historical moments and so on. Moreover, reanalysis images are weighted cascaded and input to ConvLSTM on the basis of the correlation between reanalysis data and the moving angle and distance of the typhoon. And, the Spatial Attention and weighted calculation of memory cells are added to improve the performance of ConvLSTM. This paper predicted typhoon tracks in 12 h, 18 h, 24 h and 48 h with recursive multi-step prediction. Their MAEs were 102.14 km, 168.17 km, 243.73 km and 574.62 km, respectively, which were reduced by 1.65 km, 5.93 km, 4.6 km and 13.09 km, respectively, compared with the predicted results of the improved ConvLSTM in this paper, which proved the validity of the model.

An improved TLD algorithm with selective detection

This paper proposes an improved TLD algorithm with selective detection. In the tracking module, KCF is used as the short-term tracker, and the idea of backward tracking is introduced to judge the accuracy of the result, when the result is inaccurate, the proposed algorithm starts up the detection module; in the detection module, H component of the color image is used as the input, and the mean filter serves as the first layer of the cascaded classifier, the color histogram similarity measure method is used to judge the accuracy of the detection results; finally a fusion strategy is formulated according to the output results of the tracking module and the detection module. The experiments carried on the OTB-2013 data platform show that the tracking accuracy and success rate of the proposed algorithm are respectively 0.801 and 0.591, which are 20.4% and 16.5% higher than the TLD algorithm, besides the proposed algorithm have good adaptability in scenarios such as illumination change, occlusion, and scale change, which shows superior tracking robustness.

실시간 평균 이동 추적 알고리즘의 성능 개선을 위한 히스토그램 평활화 기반 색-공간 양자화 기법

커널 기반 평균 이동 물체 추적(kernel-based mean-shift object tracking) 방법은 신뢰할 수 있는 물체 추적의 실시간 구현이 가능하기 때문에 최근 많은 관심을 받고 있다. 이 알고리즘은 표적 모델과 표적 후보 간의 히스토그램 유사성 비교를 통해 최적의 평균이동 벡터를 찾는데, 실시간 구현을 위해 대부분의 알고리즘에서는 색-공간의 균일 양자화를 수행한다. 하지만, 영상의 명암 분포가 편중되어 있는 경우 색-공간의 양자화 후 히스토그램 분포가 몇 몇 빈에 집중되기 때문에 히스토그램 유사성 비교의 정확도를 감소시키게 되고, 따라서 추적의 성능이 저하될 수 있다. 이러한 문제를 해결하기 위해 히스토그램 빈을 적응적으로 조절하는 비-균일 양자화 알고리즘이 제안되었으나 높은 복잡도로 인해 실시간 추적 알고리즘에 부적합한 단점을 갖고 있다. 이에 본 논문에서는 표적 모델에 대한 히스토그램 평활화를 수행한 후 색-공간의 균일 양자화를 수행하는 형태의 고속 비-균일 양자화 기법을 제안함으로써, 색-공간 양자화 후에도 표적 모델의 명암 분포가 전 색-영역에 고르게 분포되도록 함으로써 실시간 평균 이동 추적 기법의 추적 성능이 개선될 수 있도록 하였다. 제안하는 색-공간 양자화 기법을 통해 표적 모델과 비교 후보군 사이에 비교 대상이 되는 색 요소가 증가하게 되며, 보다 정확도 높은 히스토그램 유사성 결과를 얻을 수 있었다. 물체 추적용 영상을 통한 실험 결과, 제안하는 알고리즘은 복잡도 증가가 거의 발생하지 않는 동시에, 기존 비-균일 양자화 알고리즘 결과와 유사하거나 좀 더 나은 추적 결과를 보여주었다. Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.

Occlusion detection using horizontally segmented windows for vehicle tracking

This paper proposes an efficient algorithm for detecting occlusions in a video sequences of ground vehicles using color information. The proposed method uses a rectangular window to track a target vehicle, and the window is horizontally divided into several sub-regions of equal width. Each region is determined to be occluded or not based on the color histogram similarity to the corresponding region of the target. The occlusion detection results are used in likelihood computation of the conventional tracking algorithm based on particle filtering. Experimental results in real scenes show that the proposed method finds the occluded region successfully and improves the performance of the conventional trackers.

Video Object Segmentation and Tracking Framework With Improved Threshold Decision and Diffusion Distance

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multibackground model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multibackground model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

Visual Object Tracking Based on Local Steering Kernels and Color Histograms

In this paper, we propose a visual object tracking framework, which employs an appearance-based representation of the target object, based on local steering kernel descriptors and color histogram information. This framework takes as input the region of the target object in the previous video frame and a stored instance of the target object, and tries to localize the object in the current frame by finding the frame region that best resembles the input. As the object view changes over time, the object model is updated, hence incorporating these changes. Color histogram similarity between the detected object and the surrounding background is employed for background subtraction. Experiments are conducted to test the performance of the proposed framework under various conditions. The proposed tracking scheme is proven to be successful in tracking objects under scale and rotation variations and partial occlusion, as well as in tracking rather slowly deformable articulated objects.

Probabilistic Tracking Method Based on the Spatial Bin-Ratio Information

We proposed a new similarity measure method which could be used within the framework of probability trackers based on the particle filter to track the object. In the particle filter framework, the state transition model is chosen as the simple second-order auto-regressive mode, and the state measure is chosen as the BBRS (Blocks-Bin-Ratio-Similarity). The BBRS considers both the spatial information and the ratios between bin values of histograms. The simulation experiment was made to compare with the similarity measures based on color-histogram and the Bin-Ratio Similarity, and the tracking results in the videos showed that the BBRS-based similarity measure was more discriminative than the color histogram similarity measure in robustly tracking the object in challenging videos where the appearance and motion are drastically changing over time.

Colour Histogram Algorithms for Visual Robot Control

This paper evaluates the potential of color histogram techniques for the approach phase in the visual servoing of a camera mounted on a robot arm. We propose two complementary methods to detect, locate, and estimate the size of a target object in video images in order to extract basic visual features for robotic control. The Color Histogram Similarity method identifies the object and its location by comparing the color histogram of a search window with the histogram of a reference object. It can be used to estimate the size of the object which varies strongly when the camera approaches or moves away from the object. However this estimation is coarse and computationally intense. We introduce a new technique, the Color Histogram Footprint that creates a binary image where the outline of the object stands out, like a footprint. Using this imprint, it becomes easy to determine the precise size and location of the target object. The combination of these two techniques permits tracking of video target objects in real time.