A computer-implemented method of detecting abnormal traffic events in a geographical location, the method comprising: determining traffic objects in real time based on a traffic environment in the geographical location, wherein the traffic environment is obtained from image processing devices located in the geographic location, and comprises vehicular traffic and pedestrian traffic; determining a traffic activity in real time by tracking the traffic objects in the geographical location based on object boundaries associated with the traffic objects, wherein the traffic activity comprises a plurality of object trajectories including location, position, and the object boundaries associated with the traffic objects; detecting anomalous object trajectories of the plurality of object trajectories in real-time based on a predefined threshold, the anomalous object trajectories being associated with anomalous traffic objects of the traffic objects that cause the abnormal traffic events , wherein detecting the anomalous object trajectories comprises: generating one or more non-anomalous object trajectories from historical traffic environment; determining a reconstruction error in object trajectories in the traffic environment of the plurality of object trajectories, and detecting the anomalous object trajectories when the reconstruction error in the object trajectories in the traffic environment exceeds the predefined threshold, wherein the predefined threshold is learnt autonomously from a historical traffic environment using reward based optimization model; and detecting the abnormal traffic events in the geographical location by detecting the anomalous object trajectories from the traffic activity. determining traffic objects in real time based on a traffic environment in the geographical location, wherein the traffic environment is obtained from image processing devices located in the geographic location, and comprises vehicular traffic and pedestrian traffic; determining a traffic activity in real time by tracking the traffic objects in the geographical location based on object boundaries associated with the traffic objects, wherein the traffic activity comprises a plurality of object trajectories including location, position, and the object boundaries associated with the traffic objects; detecting anomalous object trajectories of the plurality of object trajectories in real-time based on a predefined threshold, the anomalous object trajectories being associated with anomalous traffic objects of the traffic objects that cause the abnormal traffic events , wherein detecting the anomalous object trajectories comprises: generating one or more non-anomalous object trajectories from historical traffic environment; determining a reconstruction error in object trajectories in the traffic environment of the plurality of object trajectories, and detecting the anomalous object trajectories when the reconstruction error in the object trajectories in the traffic environment exceeds the predefined threshold, wherein the predefined threshold is learnt autonomously from a historical traffic environment using reward based optimization model; and generating one or more non-anomalous object trajectories from historical traffic environment; determining a reconstruction error in object trajectories in the traffic environment of the plurality of object trajectories, and detecting the anomalous object trajectories when the reconstruction error in the object trajectories in the traffic environment exceeds the predefined threshold, wherein the predefined threshold is learnt autonomously from a historical traffic environment using reward based optimization model; and detecting the abnormal traffic events in the geographical location by detecting the anomalous object trajectories from the traffic activity. <EOS>