A Binary Image Sensor for Motion Detection

2000 ◽  
Vol 12 (5) ◽  
pp. 508-514
Author(s):  
Tomohiro Nezuka ◽  
◽  
Takafumi Fujita ◽  
Makoto Ikeda ◽  
Kunihiro Asada
Keyword(s):  
Motion Detection ◽  
Motion Vector ◽  
Cmos Technology ◽  
Image Sensor ◽  
Binary Image ◽  
Block Matching ◽  
Absolute Difference ◽  
Current Frame ◽  
Previous Frame ◽  
Exclusive Or

This Paper proposes a binary image sensor for motion detection. The sensor detects motion vectors using block-matching method. Each pixel of the image sensor has a shift register. Exclusive OR(XOR) circuits in each pixel compare the shifted image of the previous frame and the image of the current frame. The outputs of the XOR circuits are converted to current value. The current-sum circuits calculate the current-sum of pixel outputs. The current-sum of a matching block represents the sum of absolute difference. The motion vector is obtained by routine of shifting image of the previous frame and comparing the sum of absolute difference. The chip was fabricated using 1.2um 2-Metal 2-Poly-Si CMOS technology and composed of 32 × 32 pixel array and peripheral circuits in a 7.3mm × 7.3mm die.

HIGH PERFORMANCE, LOW-COMPLEXITY LINE-BASED MOTION ESTIMATION ALGORITHM WITH SMOOTHING AND PREPROCESSING

2009 ◽  
Vol 23 (01) ◽  
pp. 101-114 ◽  
Author(s):  
LI WERN CHEW ◽  
WAI CHONG CHIA ◽  
LI-MINN ANG ◽  
KAH PHOOI SENG
Keyword(s):  
Motion Estimation ◽  
High Performance ◽  
Reference Frames ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Block Matching ◽  
Absolute Difference ◽  
Current Frame ◽  
Block Matching Algorithm ◽  
The Difference

This paper introduces a smoothing and preprocessing (S+P) technique for a line-based one-bit-transform (1BT) motion estimation scheme. In the proposed algorithm, a smoothing threshold ( Threshold S) is incorporated into the 1BT convolutional kernel. By using the smoothing threshold, scattering noise which is a common problem in most 1BT images can be greatly reduced. After the transformation, the 1BT images for the current and reference frames are divided into a number of macroblocks. The macroblock in the current frame is first compared with the macroblock at the same position in the reference frame. If the Sum of Absolute Difference (SAD) is below a certain preprocessing threshold ( Threshold P), the macroblock in the current frame is considered to have negligible movement and motion search is not performed. Simulation results show that this technique achieves high performance and greatly reduces the number of search operations. By incorporating the S+P technique, the PSNR achieved by the 1BT is approaches the performance of the 8-bit Full Search Block Matching Algorithm (FSBMA), and the difference is as low as 0.08 dB. In addition, this technique outperforms current state-of-the-art 1BT motion estimation techniques. An improvement in PSNR performance by up to 0.6 dB and a reduction in the number of search operations by 60% to 93% is achieved using video conferencing sequences.

scholarly journals Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2461 ◽  
Author(s):  
Cong Zhang ◽  
Dongguang Li
Keyword(s):  
Motion Vector ◽  
Computational Cost ◽  
Infrared Image ◽  
Image Sensor ◽  
Projection Algorithm ◽  
Video Stabilization ◽  
Sensors And Actuators ◽  
Simulation And Experiment ◽  
Transformation Relation ◽  
Trajectory Correction

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze’s workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile’s motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze.

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.

Log polar image sensor in CMOS technology

1996 ◽  
Author(s):  
Danny Scheffer ◽  
Bart Dierickx ◽  
Fernando Pardo ◽  
Jan Vlummens ◽  
Guy Meynants ◽  
...  
Keyword(s):  
Cmos Technology ◽  
Image Sensor
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