Intelligent Transportation Systems (ITS) allow us to have high quality traffic information to reduce the risk of potentially critical situations. Conventional image-based traffic detection methods have difficulties acquiring good images due to perspective and background noise, poor lighting and weather conditions. In this paper, we propose a new method to accurately segment and track vehicles. After removing perspective using Modified Inverse Perspective Mapping (MIPM), Hough transform is applied to extract road lines and lanes. Then, Gaussian Mixture Models (GMM) are used to segment moving objects and to tackle car shadow effects, we apply a chromacity-based strategy. Finally, performance is evaluated through three different video benchmarks: own recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas); and two well-known public datasets (KITTI and DETRAC). Our results indicate that the proposed algorithms are robust, and more accurate compared to others, especially when facing occlusions, lighting variations and weather conditions.

Beside numerous extensive studies on Anophelinae mosquitoes of Iran, little is known on Aedes species in the country and existing reports are dispersed. The objective of this study was to identify adults of Culicinae species occurring in the Sistan and Baluchestan Province, southeast of Iran, during 2012–2014. Mosquito collections were carried out three times (May–June, September, October–November) in four counties by Centers for Diseases Control (CDC) light traps and human landing catches outdoors and under bed nets baited with carbon dioxide. These trapping were carried out two consecutive nights during the field studies. Several mosquito collections were also conducted with aspirator and pyrethrum spray space catches during the day. A total of 1885 mosquitoes were collected, belonging to 10 species of genus Culex including Cx. pipiens Complex, Cx. laticinctus, Cx. sinaiticus, and Cx. tritaeniorhynchus, one species of the genus Culiseta, Cs. longiareolata, and five species of the genus Aedes, Ae. caspius, Ae. vexans, Ae. detritus, Ae. albopictus, and Ae. unilineatus. Ae. vexans was the dominant species in the area (77.7%). During the study, seven Ae. unilineatus were collected in two villages near the city of Chabahar located in a coastal area; this is the first record for Iran and identification was confirmed by Cytochrome oxidase (COI) sequences analysis. Confirmation of the presence of Ae. unilineatus in the country raises the number of species of the genus Aedes to 12. The detection of this species reveals its probable establishment in the southeast of the country, which has implications for public health such as dengue and Zika infections and requires active entomological surveillance and implementation of adapted vector control measures in the area.

Cutaneous Leishmaniasis (CL) is the most important health problem in the city of Bushehr, southwestern Iran. The objective of the study was to determine some ecological aspects of sand flies in the city during 2010-2011. Sand flies were collected monthly from outdoors and indoors by sticky traps at four selected districts of the city. They were also dissected and examined by Nested-PCR for identification of the parasite during August- September of 2011. A total of 1234 adult sand flies were collected and 6 species including 3 of Genus Phlebotomus and 3 of Genus Sergentomyia were identified. Four species including P. papatasi (3.98 %), P. sergenti (1.14%), S. tiberiadis (87.18%) and S. baghdadis (7.7%) were found indoors. Six species including P. papatasi (3.47%), P. sergenti (3.17%), P. alexandri (0.1%), S. tiberiadis (77.74 %), S. baghdadis (15.41 %) and one female of S. clydei (0.11%) were collected from outdoors. Sand flies started to appear from March and disappear at the end of January. There was only one peak in the density curve in July. The study revealed that S. tiberiadis and S. baghdadis could enter indoors which 89 % and 81.8 % of them were found blood-fed respectively. Moreover, P. papatasi, S. tiberiadis and S. baghdadis were active indoors and outdoors in most months of the year. Nested-PCR of P. papatasi females was positive against kinetoplast DNA of L. major and L. turanica and also mixed natural infections were found by L. gerbilli and L. turanica. Moreover mixed infections by L. major and L. turanica were observed in this species. Sergentomyia clydei and S. tiberiadis were found to be negative to any DNA of Leishmania species. Phlebotomus sergenti females were found infected with DNA of L. turanica and this is the first report of natural infection and detection of the parasite from this sand fly species in worldwide.

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles are a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.) or dusty weather in arid and semiarid regions or at night, among others. In this paper, we proposed a method to track and count vehicles in dusty weather with a vibrating camera. For this purpose, we used a background subtraction based strategy mixed with extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result. Our proposed method was tested on several video surveillance records in different conditions such as in dusty or foggy weather, with a vibrating camera, and on roads with medium-level traffic volumes. The results showed that the proposed method performed better than other previously published methods, including the Kalman filter or Gaussian model, in different traffic conditions.

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions.

The introduction of new techniques to improve the robustness and accuracy of vehicle detection is always important for the intelligent transportation system as it may face different problems and challenges. Conventional image-based vehicle detection methods have presented difficulties in acquiring good images due to perspective and background noise, poor lighting and weather conditions. We propose a high-accurate, vehicle detection method by using Modified Inverse Perspective Mapping. Thus, the perspective effect is removed, and then the Hough transform was applied to extract road lines and lanes. Gaussian Mixture Model and chromaticity-based strategy were applied to segment the moving vehicles and tackle shadow effects, respectively. We evaluated the performance of the proposed method under recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas). Results indicate that the proposed approach is feasible, and more accurate compared to others, especially when facing bad weather conditions and lighting variations in different environments.