Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newtonxe2x80x99s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and groundtruth times are almost within a certain range from xe2x88x92150 s to 150 s. The reverse accuracy of the thermalfoot model for estimating departure time at onethird, onehalf, twothirds, threefourths, fourfifths, and fivesixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for peoplexe2x80x99s health, social security, and stability.

FPGAbased Accurate Pedestrian Detection with Thermal Camera for Surveillance System A surveillance system requires to achieve high accuracy of object detection at all times and to meet realtime processing requirements (30 frames per second) with high energyefficiency. Since thermal cameras allow to see even in darkness unlike a RGB camera, object detection with a thermal camera obtains higher accuracy in the night, and thereby it has attracted much attention. However, since it is challenging to extract informative features from a thermal image, implementation challenges of an object detection with high accuracy remain. To meet the requirements, we present a sparse YOLOv2based pedestrian detector with a thermal camera on an FPGA. For high accuracy, we propose a preprocessing that concatenates a thermal image with the background subtracted one for a detector to extract more informative features. Also, we develop a zero weight skipping architecture dedicated to our detector that contains a vectorizing unit that packs successive valid values into the same memory address to realize high parallel degree calculation. It leads to meet the realtime processing requirement with high energyefficiency. Compared with a conventional one, Fscore was 29 points higher, and speed was 3.3 times faster. Therefore, our system is more suitable for surveillance systems.