On-road vehicle detection: a review Zehang Sun; Bebis, G.; Miller, R.
IEEE transactions on pattern analysis and machine intelligence,
05/2006, Letnik:
28, Številka:
5
Journal Article
Recenzirano
Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In ...these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.
Abandoned tailings are one of the most important sources of heavy metal pollution in the areas surrounding mining districts, and significant leaching of heavy metals could be brought by ...precipitation. This study investigated the leaching of heavy metals from the tailings of a small-scale abandoned polymetallic mine in south China by rainwater with batch and column tests and evaluated the associated environmental impact. The mean contents of Cr, Ni, Cu, Zn, As, Cd, and Pb in the un-weathered mine tailings were 1.46×102, 3.11×102, 4.10×103, 2.18×104, 2.82×102, 5.65×102, and 8.74×103mg/kg, respectively, and appreciable fractions of Cd, Zn, Cu, and Cr in the tailings were present in the acid soluble form. Batch and column leaching tests consistently showed that significant quantities of heavy metals could be released from the mine tailings. Based on the results of column leaching tests, it was estimated that the average fluxes of Cr, Ni, Cu, Zn, As, Cd, and Pb from the mine tailings at the studied mining district leached by precipitation were 3.20, 38.3, 12.5, 1.52×104, 104, 1.08, and 9.26g/ha/yr, respectively. The metal-rich tailing leachate would impact the quality of surface water and soils downhill of the mining district, and pose significant potential ecological risk to the farmland soils, which are irrigated by local surface water. These findings indicate the importance of tailings as a source of heavy metals in the mining districts of south China with heavy precipitation, as well as the need for mitigating the releases of heavy metals and the associated environmental impact from abandoned mine tailings.
Display omitted
•Mine tailings are an important source of heavy metals to the surrounding environment•Leaching of heavy metals from mine tailings was studied with batch and column tests•>5% of Cd and Zn, >1% of Cu and Cr in the tailings were present in acid soluble form•Rainwater could leach significant quantities of heavy metals from the mine tailings•Leachate from mine tailings could cause accumulation of heavy metals in local soils
Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, ...self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.
Although metal ore mining activities are well known as an important source of heavy metals, soil pollution caused by small-scale mining activities has long been overlooked. This study investigated ...the pollution of surface soils in an area surrounding a recently abandoned small-scale polymetallic mining district in Guangdong province of south China. A total of 13 tailing samples, 145 surface soil samples, and 29 water samples were collected, and the concentrations of major heavy metals, including Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb, and Se, were determined. The results show that the tailings contained high levels of heavy metals, with Cu, Zn, As, Cd, and Pb occurring in the ranges of 739–4.15 × 103, 1.81 × 103–5.00 × 103, 118–1.26 × 103, 8.14–57.7, and 1.23 × 103–6.99 × 103 mg/kg, respectively. Heavy metals also occurred at high concentrations in the mine drainages (15.4–17.9 mg/L for Cu, 21.1–29.3 mg/L for Zn, 0.553–0.770 mg/L for Cd, and 1.17–2.57 mg/L for Pb), particularly those with pH below 3. The mean contents of Cu, Zn, As, Cd, and Pb in the surface soils of local farmlands were up to 7 times higher than the corresponding background values, and results of multivariate statistical analysis clearly indicate that Cu, Zn, Cd, and Pb were largely contributed by the mining activities. The surface soils from farmlands surrounding the mining district were moderately to seriously polluted, while the potential ecological risk of heavy metal pollution was extremely high. It was estimated that the input fluxes from the mining district to the surrounding farmlands were approximately 17.1, 59.2, 0.311, and 93.8 kg/ha/yr for Cu, Zn, Cd, and Pb, respectively, which probably occurred through transport of fine tailings by wind and runoff, and mine drainage as well. These findings indicate the significant need for proper containment of the mine tailings at small-scale metal ore mines.
Display omitted
•Heavy metal pollution in a small-scale metal ore mining area was investigated.•Pollution generally decreased with increasing distance from the mining district.•Cu, Zn, Cd, and Pb in local farmland soils came primarily from the mining activities.•Heavy metal pollution of local farmland soils posed high potential ecological risk.•Significant attention needs to be paid to the dumped tailings of small-scale mines.
Metallic bismuth has drawn attention as a promising alloying anode for advanced potassium ion batteries (PIBs). However, serious volume expansion/electrode pulverization and sluggish kinetics always ...lead to its inferior cycling and rate properties for practical applications. Therefore, advanced Bi‐based anodes via structural/compositional optimization and sur‐/interface design are needed. Herein, we develop a bottom‐up avenue to fabricate nanoscale Bi encapsulated in a 3D N‐doped carbon nanocages (Bi@N‐CNCs) framework with a void space by using a novel Bi‐based metal‐organic framework as the precursor. With elaborate regulation in annealing temperatures, the optimized Bi@N‐CNCs electrode exhibits large reversible capacities and long‐duration cyclic stability at high rates when evaluated as competitive anodes for PIBs. Insights into the intrinsic K+‐storage processes of the Bi@N‐CNCs anode are put forward from comprehensive in situ characterizations.
A hierarchical nano Bi@N‐doped carbon nanocage framework with an interior void space was smartly fabricated as competitive anode for K‐ion batteries. Its intrinsic potassium storage behaviors are unveiled via comprehensive in situ characterizations.
Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our ...focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera.
Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The ...main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases.
Mesenchymal stem cells (MSCs) selectively differentiate into adipocytes or osteoblasts, and several molecules control the fate determination of MSCs. Understanding these key checkpoints greatly ...contributes to the ability to induce specific MSC differentiation for clinical applications. In this study, we aimed to explore whether TNF receptor-associated factor 4 (TRAF4) affects MSC adipogenic differentiation, which we previously reported that could positively regulated the osteogenic differentiation.
Western blotting and Real-time Polymerase Chain Reaction were used to detected the expression pattern of TRAF4 during adipogenic differentiation. Lentivirus was constructed to regulate TRAF4 expression, and oil red O staining and Western blotting were used to assess its role in adipogenesis, which was confirmed in vivo by implanting an MSC-matrigel mixture into nude mice. Western blotting was used to detect the activated signaling pathways, and a specific inhibitor and agonist were used to clear the roles of the key signaling pathways. Additionaly, Co-Immunoprecipitation was conducted to find that Pyruvate kinase isozyme type M2 (PKM2) interacts with TRAF4, and to further explore their binding and functional domains. Finally, an RNA-binding protein immunoprecipitation assay and Western blotting were used to detect whether N6-methyladenosine mediates the decreased TRAF4 expression during adipogenic differentiation.
The results demonstrated that TRAF4 negatively regulates MSC adipogenesis in vitro and in vivo. Mechanistically, we revealed that TRAF4 binds to PKM2 to activate the kinase activity of PKM2, which subsequently activates β-catenin signaling and then inhibits adipogenesis. Furthermore, TRAF4 downregulation during adipogenesis is regulated by ALKBH5-mediated N6-methyladenosine RNA demethylation.
TRAF4 negatively regulates the adipogenesis of MSCs by activating PKM2 kinase activity, which may act as a checkpoint to fine-tune the balance of adipo-osteogenic differentiation, and suggests that TRAF4 may be a novel target of MSCs in clinical use and may also illuminate the underlying mechanisms of bone metabolic diseases.
This study was supported by the National Natural Science Foundation of China (81871750 and 81971518) and the Science and Technology Project of Guangdong Province (2019B02023600 and 2017A020215070).
PDF
