An object tracking-based behavior analysis technique for identifying TraumaticBrain Injury (TBI) among rodents is proposed. TBI is typically caused by externalfactors, resulting in severe structural brain damage, severely impacting cognitivereasoning and the overall functioning of the brain. Most existing methods usedto identify post-TBI behavior use expensive methods in highly constrained environments like functional Magnetic Resonance Imaging (fMRI) to generate brainscans which are used to identify the nature and assess the extent of the TBI. Incontrast, we propose a visual tracking-based method that computes the trajectoryof the rodent and quantifies the differences in pre-TBI and post-TBI behavior viaanalysis of the variations in motion trajectories to assess the extent of TBI ina given rodent. We explore several feature representation schemes and machinelearning models to capture the subtle cues that signify the variations in the motiontrajectory with encouraging results.