Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance

Automated Speed and Lane Change decision-making Model using Support Vector Machine

One of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model

ON VERTEX BALANCE INDEX SET OF SOME GRAPHS Communicated by Jamshid Moori

Abstract. Let Z2 = {0, 1} and G = (V, E) be a graph. A labeling f : V −→ Z2 induces an edge labeling f A labeling f is said to be vertex-friendly if | v(0) − v(1) |≤ 1. The vertex balance index set is defined by {| e f (0) − e f (1) | : f is vertex-friendly}. In this paper we completely determine the vertex balance index set of Kn, Km,n, Cn × P2 and Complete binary tree

On vertex balance index set of some graphs

Let Z2 = {0, 1} and G = (V, E) be a graph. A labeling f : V −→ Z2 induces an edge labeling f ∗ : E −→ Z2 defined by f ∗ (uv) = f(u).f(v). For i ∈ Z2, let vf (i) = v(i) = card{v ∈ V : f(v) = i} and ef (i) = e(i) = card{e ∈ E : f ∗ (e) = i}. A labeling f is said to be vertex-friendly if | v(0)−v(1) |≤ 1. The vertex balance index set is defined by {| ef (0) − ef (1) | : f is vertex-friendly}. In this paper we completely determine the vertex balance index set of Kn, Km,n, Cn × P2 and Complete binary tree

The ozonesonde intercomparison experiment at Thumba

An Indo-Soviet collaborative experiment on Ozonesonde Intercomparison was conducted at TERLS in March 1983. Thirteen rocket ozonesondes, eleven balloon ozonesondes and seven meteorological rockets were launched from Thumba. The rocket and balloon soundings were supported by on site Dobson spectrophotometric observations, surface ozone measurements as well as measurements with a Volz type filter photometer. The programme has yielded data on ozone vertical profiles from eleven rocketsondes, seven balloon-sondes and four sets of Umkehr observations. The data is studied with a view to intercompare the various sensors