Using low-level optical flow to efficiently identify the driving state in automatic driving

Context-Dependent Object Proposal and Recognition

Symmetry ◽

10.3390/sym12101619 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1619

Author(s):

Ray-I Chang ◽

Chao-Lung Ting ◽

Syuan-Yi Wu ◽

Peng-Yeng Yin

Keyword(s):

Object Recognition ◽

Support Vector ◽

Scale Invariant ◽

Edge Image ◽

Low Level ◽

Automatic Driving ◽

Object Proposal ◽

Context Dependent ◽

Scale Invariant Feature ◽

Rule Out

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

Download Full-text

Occlusion-Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints

Future Internet ◽

10.3390/fi10100092 ◽

2018 ◽

Vol 10 (10) ◽

pp. 92 ◽

Cited By ~ 2

Author(s):

Qianru Teng ◽

Yimin Chen ◽

Chen Huang

Keyword(s):

Optical Flow ◽

Empirical Evaluation ◽

Depth Estimation ◽

Bundle Adjustment ◽

Geometric Constraints ◽

Camera Motion ◽

Low Level ◽

Unsupervised Neural Network ◽

Consistency Constraints ◽

Monocular Depth

We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin.

Download Full-text

Tryptophan-Niacin Metabolism in Rat with Puromycin Aminonucleoside-Induced Nephrosis

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831.76.1.28 ◽

2006 ◽

Vol 76 (1) ◽

pp. 28-33 ◽

Cited By ~ 7

Author(s):

Yukari Egashira ◽

Shin Nagaki ◽

Hiroo Sanada

Keyword(s):

Body Weight ◽

Urinary Excretion ◽

Enzyme Activities ◽

Treated Group ◽

Control Group ◽

Puromycin Aminonucleoside ◽

Low Level ◽

Key Enzyme

We investigated the change of tryptophan-niacin metabolism in rats with puromycin aminonucleoside PAN-induced nephrosis, the mechanisms responsible for their change of urinary excretion of nicotinamide and its metabolites, and the role of the kidney in tryptophan-niacin conversion. PAN-treated rats were intraperitoneally injected once with a 1.0% (w/v) solution of PAN at a dose of 100 mg/kg body weight. The collection of 24-hour urine was conducted 8 days after PAN injection. Daily urinary excretion of nicotinamide and its metabolites, liver and blood NAD, and key enzyme activities of tryptophan-niacin metabolism were determined. In PAN-treated rats, the sum of urinary excretion of nicotinamide and its metabolites was significantly lower compared with controls. The kidneyα-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD) activity in the PAN-treated group was significantly decreased by 50%, compared with the control group. Although kidney ACMSD activity was reduced, the conversion of tryptophan to niacin tended to be lower in the PAN-treated rats. A decrease in urinary excretion of niacin and the conversion of tryptophan to niacin in nephrotic rats may contribute to a low level of blood tryptophan. The role of kidney ACMSD activity may be minimal concerning tryptophan-niacin conversion under this experimental condition.

Download Full-text