Real-time implementation of fast discriminative scale space tracking algorithm

Abstract

Real-time object tracking is an important step of many modern image processing applications. The efficient hardware design of real-time object tracker must achieve the desired accuracy while satisfying the frame rate requirements for a variety of image sizes. The existing methods of visual tracking employ sophisticated algorithms and challenge the capabilities of most embedded architectures. Discriminative scale space tracking is one algorithm that is capable of demonstrating good performance with affordable complexity. It has a high degree of parallelism which can be exploited for efficient implementation of reconfigurable hardware architectures. This paper proposes a real-time implementation of the discriminative scale-space tracker on FPGA for the major blocks. A careful design exploration of core mathematical operations of the tracking algorithm is performed to improve their hardware utilization and timing performance. Among the core functional units optimized in this work, the discrete Fourier transform achieves a computational time improvement of 92% relative to existing works, QR factorization achieves a 2.3$$\times$$ × reduction in resource utilization, and singular value decomposition yields a 3.8$$\times$$ × improvement in processing time. The proposed data path architecture is designed using Vivado HLS tool set and implemented for Zync Zed Board (xc7z020clg484-1). For an input image size of 320 $$\times$$ × 240, the proposed architecture achieves a mean 25.38 fps.

Sports Video Motion Direction Detection and Target Tracking Algorithm Based on Convolutional Neural Network

“…In the formula, p r ðwÞ is a function of the probability density of the gray level in a given figure and w is the dummy variable of the integral [21]. Then, as shown in Equation ( 5), the probability density function of the output gray level is uniform:…”

Applying Data Mining and Artificial Intelligence Techniques for High Precision Measuring of the Two-Phase Flow’s Characteristics Independent of the Pipe’s Scale Layer

“…In recent years, different advanced computational approaches have been applied in various fields of study, such as fluid mechanic engineering [20][21][22][23][24][25][26][27][28], chemical engineering [29][30][31][32][33], electrical engineering , computer engineering [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76], civil engineering [77][78][79], petroleum engineering [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95]…”

Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

“…A self-control model was developed with the power to monitor the synthesis and related system problems and to solve predictions and classifications. It is worth mentioning that, in the recent years, various mathematical approaches have been used in different research fields such as electrical and computer engineering [26][27][28][29][30], mechanical engineering [31][32][33][34], civil and urban engineering [35][36][37], biomedical engineering [38,39], industrial engineering [40][41][42][43], and physics [44,45], but among them, ANN is the most well-known and powerful numerical tool for prediction and classification [46][47][48]. Neuron values, invisible layer, effective input profile, and efficient network fabrication were determined As can be seen in Figure 4, performing the wavelet transform operation from step 5 onwards no longer provides acceptable high-frequency information, so to reduce the computational volume, the wavelet operation is performed up to the fourth step.…”