When it comes to taking the public transportation, time and patience are of essence. In other words, many people using public transport buses have experienced time loss because of waiting at the bus stops. In this paper, we proposed smart bus tracking system that any passenger with a smart phone or mobile device with the QR (Quick Response) code reader can scan QR codes placed at bus stops to view estimated bus arrival times, buses' current locations, and bus routes on a map. Anyone can access these maps and have the option to sign up to receive free alerts about expected bus arrival times for the interested buses and related routes via SMS and e-mails. We used C4.5 (a statistical classifier) algorithm for the estimation of bus arrival times to minimize the passengers waiting time. GPS (Global Positioning System) and Google Maps are used for navigation and display services, respectively.

Traffic surveillance cameras are widely used in traffic management and information systems. Processing streaming media in real time is resource and time-consuming processes and even impossible to realize in most real-world applications. To overcome the performance problems in such applications, this article introduces a middleware system based on pub/sub messaging protocol and a dispatcher to preprocess the streams in real time. Experimental results show that proposed middleware may be utilized in different areas such as infrastructure planning, traffic management, and prevention of traffic offenses.

People use various communication methods to interact with each other, such as speech and body movements. However, communication methods in babies differ according to their developmental periods. Babies may not be able to express themselves verbally, but they do have their own communication methods. Naturally, it becomes important for parents to understand these signs/poses. Human pose estimation is extensively employed in different applications such as video surveillance, sports analysis and medical support/aid. The goal of this research is to assist new parents by addressing pose-based real-time body movements of babies (such as arching back, head banging, kicking legs, rubbing eyes, stretching, sucking fingers) and making sense of their activities. This is the first study to estimate poses on babies. In this paper, a baby pose dataset is created from 156 video clips through online video sharing platforms. Key-points are obtained from three different pose estimators - OpenPose, AlphaPose, and KAPAO. Different LSTM models are used to recognize the babies’ activities and different performance metrics are used to compare these models. The best model has 99% accuracy and 0.0712 loss ratio. Also, babies are tracked in real-time via DeepSORT algorithm. Experimental results show that the proposed system is very promising and filling a gap in making sense of baby poses and monitoring them.