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Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur. This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.

Modelling of complex non-linear systems using the process data is a challenging issue. This paper presents the dynamic fuzzy modelling of a cooling coil system using the input output process data. The structure of the model is kept fixed as zero-order Ta kagi-Sugeno (TS) fuzzy nonlinear output error (NOE) model. The parameter identification is done using the recursive least square (RLS) technique. There are three inputs to the system and a single output i.e. a MISO system. The modelling is carried out in three steps i.e. offline parameter identification, the online parameter identification and then dynamic modelling. Simulation results have been presented which demonstrate the efficiency of dynamic modelling with online parameter identi fication as compared to the techniques. The online models are extremely useful in model based control techniques.